Trust in Science Analysis

Load Data

# results <- read.csv("data_clean.csv")
results <- read.csv("../data_clean.csv")

# trust in data is the column: bar-data_6
# trust in vis is the column: bar-vis_6

ggplot(aes(x=complexity,y=data.trust_4),data=results)+
 geom_bar(stat = "summary", fun = "mean")+
  
   theme_minimal() +
  theme(panel.spacing = unit(2, "lines"))

# levels(results$isCovidData) <- list('0'="Crop Diseases", '1'="COVID")
results %>%
  group_by(complexity,isCovidData) %>%
  summarize(n = n(), 
            se = sd(vis.trust_6)/sqrt(n),
            vis.trust_6 = mean(vis.trust_6),
            n = n)%>%
    ggplot(aes(x = vis.trust_6, y = 0, xmax= vis.trust_6+se, xmin = vis.trust_6-se, colour = as.factor(isCovidData))) +
  geom_point()+
  geom_jitter(aes(x=vis.trust_6, y=0),data=results)+
  geom_errorbar(height=0.3)+
  ylim(-1,1)+
  xlim(1, 7)+
  # labs(title = "Trust in data") + 
  facet_grid(complexity~isCovidData)+
  #facet_grid(rows = vars(complexity), cols = vars(chartType)) +
  theme_minimal()

`summarise()` has grouped output by 'complexity'. You can override using the `.groups` argument.Warning: Ignoring unknown parameters: `height`

Error in `geom_jitter()`:
! Problem while computing aesthetics.
ℹ Error occurred in the 2nd layer.
Caused by error in `FUN()`:
! object 'se' not found
Backtrace:
  1. base (local) `<fn>`(x)
  2. ggplot2:::print.ggplot(x)
  4. ggplot2:::ggplot_build.ggplot(x)
  5. ggplot2:::by_layer(...)
 12. ggplot2 (local) f(l = layers[[i]], d = data[[i]])
 13. l$compute_aesthetics(d, plot)
 14. ggplot2 (local) compute_aesthetics(..., self = self)
 15. base::lapply(aesthetics, eval_tidy, data = data, env = env)
 16. rlang (local) FUN(X[[i]], ...)

Trust in Vis

# levels(results$isCovidData) <- list('0'="Crop Diseases", '1'="COVID")
results %>%
  ggplot(aes(x = vis.trust_6, y = 0, colour = as.factor(isCovidData))) +
  geom_violin(scale = "width", trim = FALSE, aes(fill = as.factor(isCovidData)), colour=NA, alpha = 0.3)+
  geom_jitter( height = 0.05, width = 0.2, alpha = 0.2) +
  xlim(1, 7)+
  # labs(title = "Trust in data") + 
  geom_segment(data = results %>% 
                group_by(complexity, isCovidData) %>%
                summarize(n = n(), 
                          vis.trust_6 = mean(vis.trust_6)), 
              aes(xend = vis.trust_6, yend=(0 -.35), y=(0 +.35),colour = as.factor(isCovidData)),size=1) +
  facet_grid(complexity~isCovidData)+
  #facet_grid(rows = vars(complexity), cols = vars(chartType)) +
  theme_minimal()

`summarise()` has grouped output by 'complexity'. You can override using the `.groups` argument.Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
Please use `linewidth` instead.

# levels(results$isCovidData) <- list('0'="Crop Diseases", '1'="COVID")
results %>%
  ggplot(aes(x = data.trust_6, y = 0)) +
  geom_violin(aes(fill = data.trust_6), colour=NA, alpha = 0.3)+
  geom_jitter(data = results, width = 0.2, alpha = 0.2) +
  xlim(1, 7)+
  # labs(title = "Trust in data") + 
  geom_segment(data = results %>% 
                group_by(complexity) %>%
                summarize(n = n(), 
                          data.trust_6 = mean(data.trust_6)), 
              aes(xend = data.trust_6, yend=(0 -.35), y=(0 +.35),colour = data.trust_6),size=1) +
  facet_grid(complexity~.)+
  #facet_grid(rows = vars(complexity), cols = vars(chartType)) +
  theme_minimal()

model <- lm(formula = data.trust_6 ~ data.trust_1 + data.trust_2 + data.trust_3 + data.trust_4 + data.trust_5 
           + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 
            , data = results)
summary(model)

Call:
lm(formula = data.trust_6 ~ data.trust_1 + data.trust_2 + data.trust_3 + 
    data.trust_4 + data.trust_5 + Age + Gender + State_1 + Education + 
    Parents_education + Language + Ethnicity + Income + Religion + 
    trust.in.science_7 + need_for_cognition + interpersonal.trust_1, 
    data = results)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.9931 -0.4489  0.0000  0.4795  3.9859 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)           -4.597126   6.131213  -0.750 0.453747    
data.trust_1           0.090886   0.052697   1.725 0.085226 .  
data.trust_2           0.066808   0.033936   1.969 0.049565 *  
data.trust_3           0.325323   0.043791   7.429 5.02e-13 ***
data.trust_4           0.162320   0.046931   3.459 0.000591 ***
data.trust_5           0.049840   0.043281   1.152 0.250086    
Age                    0.002397   0.003082   0.778 0.436984    
Gender                -0.088293   0.084346  -1.047 0.295719    
State_1Arizona         0.320132   0.414220   0.773 0.439987    
State_1Arkansas        0.101476   0.571594   0.178 0.859165    
State_1California      0.171603   0.290002   0.592 0.554310    
State_1Colorado        0.009551   0.402251   0.024 0.981066    
State_1Connecticut    -0.582245   0.530000  -1.099 0.272501    
State_1Delaware        0.108646   0.766072   0.142 0.887280    
State_1Florida         0.240345   0.309645   0.776 0.438014    
State_1Georgia        -0.082729   0.327358  -0.253 0.800594    
State_1Hawaii         -1.074211   0.658099  -1.632 0.103271    
State_1Illinois       -0.152864   0.346571  -0.441 0.659356    
State_1Indiana        -1.298768   0.432508  -3.003 0.002813 ** 
State_1Iowa           -0.919647   0.574463  -1.601 0.110058    
State_1Kansas          0.117401   0.461979   0.254 0.799506    
State_1Kentucky       -0.377233   0.430679  -0.876 0.381519    
State_1Louisiana      -0.230854   0.493132  -0.468 0.639898    
State_1Maine           0.411765   0.640290   0.643 0.520471    
State_1Maryland        0.620900   0.376008   1.651 0.099330 .  
State_1Massachusetts   0.309817   0.378450   0.819 0.413392    
State_1Michigan        0.080221   0.362958   0.221 0.825171    
State_1Minnesota       0.421774   0.523374   0.806 0.420713    
State_1Mississippi     0.435419   0.568735   0.766 0.444293    
State_1Missouri        0.564911   0.427374   1.322 0.186856    
State_1Montana        -0.433078   0.767901  -0.564 0.573033    
State_1Nebraska        0.333629   0.640919   0.521 0.602921    
State_1Nevada          0.080717   0.491743   0.164 0.869686    
State_1New Hampshire  -1.278375   1.057365  -1.209 0.227248    
State_1New Jersey     -0.245098   0.445964  -0.550 0.582854    
State_1New York        0.325438   0.322277   1.010 0.313094    
State_1North Carolina -0.059033   0.343942  -0.172 0.863796    
State_1Ohio            0.032602   0.368549   0.088 0.929547    
State_1Oklahoma        0.330076   0.494983   0.667 0.505192    
State_1Oregon          0.163195   0.445933   0.366 0.714552    
State_1Pennsylvania    0.340566   0.316632   1.076 0.282649    
State_1Rhode Island   -0.352997   0.644242  -0.548 0.583996    
State_1South Carolina  0.977091   0.520178   1.878 0.060933 .  
State_1South Dakota   -1.699040   1.061465  -1.601 0.110108    
State_1Tennessee      -0.717363   0.390773  -1.836 0.067010 .  
State_1Texas          -0.245762   0.302275  -0.813 0.416596    
State_1Utah            0.086623   0.490001   0.177 0.859755    
State_1Vermont        -0.535491   0.766101  -0.699 0.484901    
State_1Virginia        0.202618   0.448581   0.452 0.651698    
State_1Washington      0.025332   0.352939   0.072 0.942812    
State_1West Virginia  -0.101128   0.640160  -0.158 0.874544    
State_1Wisconsin       0.490522   0.353125   1.389 0.165448    
State_1Wyoming        -0.663340   1.046241  -0.634 0.526367    
Education             -0.034522   0.027327  -1.263 0.207097    
Parents_education      0.034826   0.062928   0.553 0.580231    
Language              -0.022958   0.177541  -0.129 0.897164    
Ethnicity             -0.016359   0.025183  -0.650 0.516260    
Income                 0.003974   0.009943   0.400 0.689553    
Religion               0.005769   0.047172   0.122 0.902717    
trust.in.science_7     0.072818   0.024259   3.002 0.002824 ** 
need_for_cognition     0.121533   0.075003   1.620 0.105806    
interpersonal.trust_1  0.099051   0.036761   2.694 0.007297 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.003 on 482 degrees of freedom
Multiple R-squared:  0.4648,    Adjusted R-squared:  0.397 
F-statistic: 6.862 on 61 and 482 DF,  p-value: < 2.2e-16

model <- lm(formula = vis.trust_6 ~ vis.trust_1 + vis.trust_2 + vis.trust_3 + vis.trust_4 + vis.trust_5 
            # + complexity + chartType + as.factor(isCovidData) + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 
            , data = results)
anova(model)

Analysis of Variance Table

Response: vis.trust_6
             Df Sum Sq Mean Sq  F value    Pr(>F)    
vis.trust_1   1 152.28 152.282 152.3443 < 2.2e-16 ***
vis.trust_2   1  50.46  50.457  50.4773 3.842e-12 ***
vis.trust_3   1  60.61  60.607  60.6314 3.558e-14 ***
vis.trust_4   1   2.42   2.419   2.4201    0.1204    
vis.trust_5   1   0.34   0.338   0.3382    0.5611    
Residuals   538 537.78   1.000                       
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

model <- lm(formula = vis.trust_3 ~  complexity  * chartType + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 , data = results%>%filter(isCovidData == 1))
anova(model)

Analysis of Variance Table

Response: vis.trust_3
                       Df Sum Sq Mean Sq F value   Pr(>F)   
complexity              2   6.18  3.0890  1.9169 0.149640   
chartType               1   0.80  0.8012  0.4972 0.481516   
Age                     1   0.42  0.4188  0.2599 0.610740   
Gender                  1   0.37  0.3662  0.2273 0.634056   
State_1                43  77.18  1.7949  1.1138 0.304544   
Education               1   1.61  1.6079  0.9978 0.319004   
Parents_education       1   1.20  1.2026  0.7462 0.388658   
Language                1   0.09  0.0923  0.0573 0.811107   
Ethnicity               1   5.34  5.3413  3.3146 0.070098 . 
Income                  1   0.00  0.0046  0.0029 0.957354   
Religion                1   0.58  0.5823  0.3613 0.548411   
trust.in.science_7      1  14.27 14.2723  8.8567 0.003264 **
need_for_cognition      1   7.70  7.7041  4.7808 0.029888 * 
interpersonal.trust_1   1   4.48  4.4798  2.7799 0.096949 . 
complexity:chartType    2   0.71  0.3566  0.2213 0.801683   
Residuals             209 336.80  1.6115                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

model <- lm(formula = vis.trust_6 ~ 
              # affect.science_1 + affect.clarity_1+  affect.aesthetic_1 
              +vis.trust_1 + 
              vis.trust_2 + vis.trust_3 + vis.trust_4 + vis.trust_5 + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 , data = results)
summary(model)

Call:
lm(formula = vis.trust_6 ~ +vis.trust_1 + vis.trust_2 + vis.trust_3 + 
    vis.trust_4 + vis.trust_5 + Age + Gender + State_1 + Education + 
    Parents_education + Language + Ethnicity + Income + Religion + 
    trust.in.science_7 + need_for_cognition + interpersonal.trust_1, 
    data = results)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0465 -0.5388  0.0353  0.5430  3.4413 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)           -9.7748166  5.6887185  -1.718   0.0864 .  
vis.trust_1            0.2177072  0.0365616   5.955 5.03e-09 ***
vis.trust_2            0.2026381  0.0385412   5.258 2.20e-07 ***
vis.trust_3            0.2543282  0.0366286   6.943 1.24e-11 ***
vis.trust_4            0.0125527  0.0210627   0.596   0.5515    
vis.trust_5            0.0193333  0.0222957   0.867   0.3863    
Age                    0.0050520  0.0028559   1.769   0.0775 .  
Gender                 0.0176812  0.0787872   0.224   0.8225    
State_1Arizona         0.3175489  0.3871480   0.820   0.4125    
State_1Arkansas        0.3995481  0.5305075   0.753   0.4517    
State_1California      0.0627264  0.2707982   0.232   0.8169    
State_1Colorado        0.0126473  0.3762153   0.034   0.9732    
State_1Connecticut    -0.3826644  0.4931821  -0.776   0.4382    
State_1Delaware        0.3077714  0.7128574   0.432   0.6661    
State_1Florida         0.1345137  0.2888798   0.466   0.6417    
State_1Georgia        -0.1394036  0.3054318  -0.456   0.6483    
State_1Hawaii          1.4015810  0.6129795   2.287   0.0227 *  
State_1Illinois        0.3379163  0.3220623   1.049   0.2946    
State_1Indiana        -0.5759068  0.4013371  -1.435   0.1519    
State_1Iowa           -0.2440088  0.5344796  -0.457   0.6482    
State_1Kansas          0.2212348  0.4305178   0.514   0.6076    
State_1Kentucky       -0.3814180  0.4018532  -0.949   0.3430    
State_1Louisiana      -0.1507965  0.4596971  -0.328   0.7430    
State_1Maine           0.4190084  0.5964512   0.703   0.4827    
State_1Maryland        0.2503893  0.3505930   0.714   0.4755    
State_1Massachusetts   0.0252048  0.3531408   0.071   0.9431    
State_1Michigan        0.1320074  0.3390382   0.389   0.6972    
State_1Minnesota      -0.1109293  0.4879777  -0.227   0.8203    
State_1Mississippi     0.5048191  0.5311894   0.950   0.3424    
State_1Missouri       -0.0611838  0.3969978  -0.154   0.8776    
State_1Montana         0.3806320  0.7136051   0.533   0.5940    
State_1Nebraska        0.1480402  0.5972284   0.248   0.8043    
State_1Nevada         -0.2088468  0.4581477  -0.456   0.6487    
State_1New Hampshire   2.5069006  0.9984953   2.511   0.0124 *  
State_1New Jersey      0.0208779  0.4155330   0.050   0.9599    
State_1New York        0.3301663  0.3007470   1.098   0.2728    
State_1North Carolina -0.0356157  0.3226234  -0.110   0.9121    
State_1Ohio            0.0394090  0.3469288   0.114   0.9096    
State_1Oklahoma        0.2977724  0.4606554   0.646   0.5183    
State_1Oregon         -0.1849322  0.4150497  -0.446   0.6561    
State_1Pennsylvania    0.0362979  0.2953940   0.123   0.9023    
State_1Rhode Island   -0.0292344  0.6001937  -0.049   0.9612    
State_1South Carolina -0.7201420  0.4862644  -1.481   0.1393    
State_1South Dakota   -3.9406791  0.9904644  -3.979 8.00e-05 ***
State_1Tennessee       0.5560222  0.3668685   1.516   0.1303    
State_1Texas          -0.2550208  0.2836090  -0.899   0.3690    
State_1Utah           -0.2173609  0.4580501  -0.475   0.6353    
State_1Vermont        -1.0913815  0.7157714  -1.525   0.1280    
State_1Virginia       -0.0295251  0.4172656  -0.071   0.9436    
State_1Washington      0.1895468  0.3306196   0.573   0.5667    
State_1West Virginia  -0.3230450  0.5970627  -0.541   0.5887    
State_1Wisconsin       0.0712548  0.3287145   0.217   0.8285    
State_1Wyoming         0.0295694  0.9744157   0.030   0.9758    
Education             -0.0233297  0.0255197  -0.914   0.3611    
Parents_education      0.0122392  0.0587525   0.208   0.8351    
Language               0.0714951  0.1649652   0.433   0.6649    
Ethnicity             -0.0009967  0.0233759  -0.043   0.9660    
Income                -0.0011081  0.0092423  -0.120   0.9046    
Religion              -0.0248503  0.0441250  -0.563   0.5736    
trust.in.science_7     0.1296320  0.0221558   5.851 9.04e-09 ***
need_for_cognition    -0.0923613  0.0709843  -1.301   0.1938    
interpersonal.trust_1  0.0782028  0.0340371   2.298   0.0220 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9339 on 482 degrees of freedom
Multiple R-squared:  0.477, Adjusted R-squared:  0.4109 
F-statistic: 7.208 on 61 and 482 DF,  p-value: < 2.2e-16

# # post-hoc tests
# aov(vis.trust_6 ~ complexity * as.factor(isCovidData), data = results) %>% tukey_hsd()
# # effect sizes
# eta_squared(aov(vis.trust_6 ~ complexity * as.factor(isCovidData) * chartType + 
#                   Age + Gender + State_1 + Education + Parents_education + Language + 
#                   Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + 
#                   interpersonal.trust_1,
#              data = results))

# ggplot(data=results%>%filter(isCovidData == 0),aes(x=complexity, y = vis.trust_5))+
#   geom_bar(stat = "summary", fun = "mean")

model <- lm(formula = vis.trust_3 ~ ordinal_complexity + as.factor(isCovidData) + chartType + as.factor(isCovidData) +
                                    + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,data = results)
# 
            
summary(model)

Call:
lm(formula = vis.trust_3 ~ ordinal_complexity + as.factor(isCovidData) + 
    chartType + as.factor(isCovidData) + +Age + Gender + State_1 + 
    Education + Parents_education + Language + Ethnicity + Income + 
    Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1, 
    data = results)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.7104 -0.7569  0.1859  0.7421  3.4686 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)             -2.253611   7.692632  -0.293 0.769681    
ordinal_complexity       0.002051   0.070072   0.029 0.976656    
as.factor(isCovidData)1  0.185278   0.113586   1.631 0.103505    
chartTypeline            0.220409   0.113093   1.949 0.051882 .  
Age                      0.002593   0.003855   0.673 0.501545    
Gender                   0.009208   0.107068   0.086 0.931501    
State_1Arizona          -0.319516   0.522306  -0.612 0.540996    
State_1Arkansas         -0.506244   0.719203  -0.704 0.481836    
State_1California       -0.328165   0.365796  -0.897 0.370098    
State_1Colorado         -0.601942   0.507100  -1.187 0.235798    
State_1Connecticut      -1.512672   0.664873  -2.275 0.023335 *  
State_1Delaware         -2.034675   0.960307  -2.119 0.034618 *  
State_1Florida          -0.598671   0.389407  -1.537 0.124851    
State_1Georgia          -0.343632   0.411245  -0.836 0.403799    
State_1Hawaii           -1.450258   0.822358  -1.764 0.078441 .  
State_1Illinois         -0.565001   0.434780  -1.300 0.194387    
State_1Indiana           0.064234   0.543400   0.118 0.905952    
State_1Iowa             -0.746785   0.720026  -1.037 0.300178    
State_1Kansas           -0.085537   0.583372  -0.147 0.883489    
State_1Kentucky         -0.404426   0.540391  -0.748 0.454586    
State_1Louisiana        -0.710200   0.619812  -1.146 0.252432    
State_1Maine            -0.002235   0.807106  -0.003 0.997792    
State_1Maryland         -0.191383   0.473393  -0.404 0.686187    
State_1Massachusetts    -0.578580   0.477665  -1.211 0.226384    
State_1Michigan          0.143354   0.458704   0.313 0.754781    
State_1Minnesota         0.202655   0.659148   0.307 0.758634    
State_1Mississippi      -0.243577   0.718865  -0.339 0.734881    
State_1Missouri         -1.034608   0.536314  -1.929 0.054302 .  
State_1Montana           0.547453   0.963102   0.568 0.570009    
State_1Nebraska         -0.757798   0.811544  -0.934 0.350886    
State_1Nevada            0.422911   0.620711   0.681 0.495986    
State_1New Hampshire    -3.021346   1.319715  -2.289 0.022486 *  
State_1New Jersey       -0.640485   0.559732  -1.144 0.253077    
State_1New York         -0.334490   0.407006  -0.822 0.411578    
State_1North Carolina   -0.778971   0.435150  -1.790 0.074059 .  
State_1Ohio             -0.898394   0.465074  -1.932 0.053977 .  
State_1Oklahoma         -0.579929   0.624106  -0.929 0.353241    
State_1Oregon           -0.136469   0.564654  -0.242 0.809126    
State_1Pennsylvania     -0.431544   0.400116  -1.079 0.281327    
State_1Rhode Island     -0.440920   0.813192  -0.542 0.587924    
State_1South Carolina   -0.362466   0.654001  -0.554 0.579678    
State_1South Dakota      2.310427   1.326014   1.742 0.082076 .  
State_1Tennessee        -0.026548   0.492469  -0.054 0.957031    
State_1Texas            -0.601043   0.382344  -1.572 0.116605    
State_1Utah             -0.138923   0.615647  -0.226 0.821566    
State_1Vermont          -1.251709   0.959143  -1.305 0.192503    
State_1Virginia         -0.740964   0.562732  -1.317 0.188554    
State_1Washington       -0.459616   0.445740  -1.031 0.302995    
State_1West Virginia    -0.796094   0.806630  -0.987 0.324166    
State_1Wisconsin        -0.437682   0.445810  -0.982 0.326705    
State_1Wyoming          -1.101072   1.323240  -0.832 0.405761    
Education               -0.040307   0.034413  -1.171 0.242064    
Parents_education        0.006272   0.079347   0.079 0.937029    
Language                 0.040903   0.223534   0.183 0.854887    
Ethnicity                0.037000   0.031572   1.172 0.241792    
Income                  -0.003775   0.012493  -0.302 0.762639    
Religion                -0.080868   0.059408  -1.361 0.174073    
trust.in.science_7       0.097985   0.029472   3.325 0.000952 ***
need_for_cognition       0.306474   0.093847   3.266 0.001169 ** 
interpersonal.trust_1    0.050323   0.045987   1.094 0.274374    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.264 on 484 degrees of freedom
Multiple R-squared:  0.1561,    Adjusted R-squared:  0.05318 
F-statistic: 1.517 on 59 and 484 DF,  p-value: 0.01063

#anova(model)


# # post-hoc tests
# aov(vis.trust_6 ~ complexity * as.factor(isCovidData), data = results) %>% tukey_hsd()
# # effect sizes
# eta_squared(aov(vis.trust_6 ~ complexity * as.factor(isCovidData) * chartType + 
#                   Age + Gender + State_1 + Education + Parents_education + Language + 
#                   Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + 
#                   interpersonal.trust_1,
#              data = results))

# ggplot(data=results,aes(x=complexity, y = data.trust_4))+
#   geom_bar(stat = "summary", fun = "mean")

Trust in science, need for cognition, and interpersonal trust on trust in Vis

results %>%
  gather(key = variables, value = values, 
         trust.in.science_7, need_for_cognition, interpersonal.trust_1) %>%
  ggplot(aes(x = values, y = vis.trust_6, color = variables)) +

  facet_grid(cols = vars(variables)) +
    geom_jitter() +
  geom_smooth(method='lm', color = "black") +
  geom_blank() + 
   theme_minimal() +
  theme(panel.spacing = unit(2, "lines"))

Trust in Data

results %>%
  ggplot(aes(x = data.trust_6, y = isCovidData, colour = as.factor(isCovidData))) +
  geom_jitter(data = results, width = 0.5) +
  # labs(title = "Trust in data") + 
  geom_vline(data = results %>% 
                group_by(complexity, isCovidData, chartType) %>%
                summarize(n = n(), 
                          data.trust_6 = mean(data.trust_6)), 
              aes(xintercept = data.trust_6, colour = as.factor(isCovidData))) +
  facet_grid(rows = vars(complexity), cols = vars(chartType)) +
  theme_minimal()

`summarise()` has grouped output by 'complexity', 'isCovidData'. You can override using the `.groups` argument.

results %>% 
  group_by(complexity, isCovidData) %>%
  summarize(n = n(), 
            mean = mean(data.trust_6),
            se = sd(data.trust_6)/sqrt(n),
            n = n)

`summarise()` has grouped output by 'complexity'. You can override using the `.groups` argument.

model <- lm(formula = data.trust_4 ~ complexity + as.factor(isCovidData) * chartType
                                    + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
            data = results)
summary(model)

Call:
lm(formula = data.trust_4 ~ complexity + as.factor(isCovidData) * 
    chartType + Age + Gender + State_1 + Education + Parents_education + 
    Language + Ethnicity + Income + Religion + trust.in.science_7 + 
    need_for_cognition + interpersonal.trust_1, data = results)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.14016 -0.53910  0.08415  0.70346  3.03907 

Coefficients:
                                        Estimate Std. Error t value Pr(>|t|)   
(Intercept)                           -14.694878   6.673542  -2.202  0.02814 * 
complexitymoderate                      0.208617   0.122872   1.698  0.09018 . 
complexitysimple                        0.358240   0.121432   2.950  0.00333 **
as.factor(isCovidData)1                 0.374204   0.138430   2.703  0.00711 **
chartTypeline                           0.034623   0.137542   0.252  0.80136   
Age                                     0.008653   0.003349   2.584  0.01006 * 
Gender                                  0.030136   0.093049   0.324  0.74618   
State_1Arizona                         -0.316997   0.452493  -0.701  0.48392   
State_1Arkansas                        -0.437386   0.623624  -0.701  0.48342   
State_1California                       0.084853   0.317651   0.267  0.78949   
State_1Colorado                         0.214593   0.441242   0.486  0.62695   
State_1Connecticut                     -1.358886   0.575400  -2.362  0.01859 * 
State_1Delaware                        -1.247992   0.835542  -1.494  0.13593   
State_1Florida                         -0.193183   0.337986  -0.572  0.56788   
State_1Georgia                          0.325580   0.356398   0.914  0.36142   
State_1Hawaii                          -1.477399   0.715209  -2.066  0.03939 * 
State_1Illinois                        -0.595969   0.376875  -1.581  0.11445   
State_1Indiana                          0.630513   0.470799   1.339  0.18112   
State_1Iowa                             0.823325   0.624874   1.318  0.18827   
State_1Kansas                          -0.703091   0.506390  -1.388  0.16565   
State_1Kentucky                        -0.452511   0.468136  -0.967  0.33422   
State_1Louisiana                        0.483371   0.537357   0.900  0.36882   
State_1Maine                            0.004888   0.699263   0.007  0.99443   
State_1Maryland                         0.057735   0.412289   0.140  0.88869   
State_1Massachusetts                   -0.111512   0.413733  -0.270  0.78764   
State_1Michigan                        -0.281781   0.398275  -0.708  0.47960   
State_1Minnesota                        0.355546   0.570884   0.623  0.53371   
State_1Mississippi                      0.287626   0.623300   0.461  0.64468   
State_1Missouri                        -1.000409   0.465709  -2.148  0.03220 * 
State_1Montana                         -0.273669   0.833881  -0.328  0.74291   
State_1Nebraska                        -0.001497   0.704053  -0.002  0.99830   
State_1Nevada                           0.506430   0.537166   0.943  0.34627   
State_1New Hampshire                    2.101587   1.145335   1.835  0.06714 . 
State_1New Jersey                       0.518351   0.484286   1.070  0.28500   
State_1New York                        -0.244350   0.352710  -0.693  0.48878   
State_1North Carolina                   0.067277   0.378387   0.178  0.85896   
State_1Ohio                             0.015661   0.402508   0.039  0.96898   
State_1Oklahoma                         0.708973   0.540732   1.311  0.19044   
State_1Oregon                          -0.212516   0.489178  -0.434  0.66417   
State_1Pennsylvania                     0.219758   0.346940   0.633  0.52676   
State_1Rhode Island                    -0.059246   0.703547  -0.084  0.93292   
State_1South Carolina                  -0.311252   0.566705  -0.549  0.58310   
State_1South Dakota                     1.315778   1.149799   1.144  0.25304   
State_1Tennessee                        0.184057   0.426611   0.431  0.66634   
State_1Texas                            0.045647   0.331397   0.138  0.89050   
State_1Utah                             0.672128   0.533353   1.260  0.20821   
State_1Vermont                          0.729948   0.831030   0.878  0.38018   
State_1Virginia                        -0.471638   0.487505  -0.967  0.33380   
State_1Washington                      -0.055164   0.385612  -0.143  0.88631   
State_1West Virginia                   -0.257382   0.699820  -0.368  0.71320   
State_1Wisconsin                        0.317597   0.385852   0.823  0.41085   
State_1Wyoming                          0.015307   1.145239   0.013  0.98934   
Education                               0.007315   0.029804   0.245  0.80622   
Parents_education                       0.049968   0.068907   0.725  0.46871   
Language                                0.142884   0.194280   0.735  0.46242   
Ethnicity                               0.063700   0.027339   2.330  0.02022 * 
Income                                  0.003806   0.010830   0.351  0.72545   
Religion                               -0.029721   0.051581  -0.576  0.56474   
trust.in.science_7                      0.037030   0.025604   1.446  0.14875   
need_for_cognition                      0.162610   0.081265   2.001  0.04595 * 
interpersonal.trust_1                   0.056205   0.039899   1.409  0.15957   
as.factor(isCovidData)1:chartTypeline   0.118515   0.201227   0.589  0.55616   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.093 on 482 degrees of freedom
Multiple R-squared:  0.2097,    Adjusted R-squared:  0.1096 
F-statistic: 2.096 on 61 and 482 DF,  p-value: 9.544e-06

# post-hoc tests
# aov(data.trust_6 ~ complexity * as.factor(isCovidData), data = results) %>% tukey_hsd()
# aov(data.trust_6 ~ chartType * as.factor(isCovidData), data = results) %>% tukey_hsd()
# # effect sizes
# eta_squared(aov(data.trust_6 ~ complexity * as.factor(isCovidData) * chartType + 
#                   Age + Gender + State_1 + Education + Parents_education + Language + 
#                   Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + 
#                   interpersonal.trust_1,
#              data = results))
# emmeans(aov(data.trust_6 ~ complexity , data = results) , ~ complexity)

Trust in science, need for cognition, and interpersonal trust on trust in Data

results %>%
  gather(key = variables, value = values, 
         trust.in.science_7, need_for_cognition, interpersonal.trust_1) %>%
  ggplot(aes(x = values, y = data.trust_6, color = variables)) +

  facet_grid(cols = vars(variables)) +
    geom_jitter() +
  geom_smooth(color = "black") +
  geom_blank() + 
   theme_minimal() +
  theme(panel.spacing = unit(2, "lines"))

results_long_data <- results %>%
  select(data.trust_1, data.trust_2, data.trust_3,
         data.trust_4, data.trust_5, data.trust_6,
         ResponseId, complexity,
         vlat_simple, vlat_moderate, vlat_complex) %>%
  gather(key = trustItemData, value = trustRatingData, 
         data.trust_1, data.trust_2, data.trust_3, data.trust_4, data.trust_5, data.trust_6)

results_long_data %>%
  ggplot(aes(x = trustRatingData, y = 1, color = complexity)) +
  geom_jitter() +
  ylim(0, 2) + 
  labs(title = "Trust in data (All)") + 
  geom_vline(data = results_long_data %>% 
               group_by(complexity, trustItemData) %>%
               summarize(n = n(), 
                         average = mean(trustRatingData)), 
             aes(xintercept = average, color = complexity)) +
  facet_grid(vars(trustItemData)) +
  theme_minimal()

`summarise()` has grouped output by 'complexity'. You can override using the `.groups` argument.

Relationship between trust in data and vis, across complexity

results_long_data <- results %>%
  select(data.trust_1, data.trust_2, data.trust_3,
         data.trust_4, data.trust_5, data.trust_6,
         ResponseId, complexity,
         chartType, isCovidData, 
         vlat_simple, vlat_moderate, vlat_complex) %>%
  gather(key = trustItemData, value = trustRatingData, 
         # data.trust_1, data.trust_2, data.trust_3, data.trust_4, data.trust_5, 
         data.trust_6)

results_long_vis <- results %>%
  gather(key = trustItemVis, value = trustRatingVis, 
         # vis.trust_1, vis.trust_2, vis.trust_3, vis.trust_4, vis.trust_5, 
         vis.trust_6)

results_long_all <- merge(results_long_vis, results_long_data, 
                          by = c("ResponseId", "complexity", "chartType", "isCovidData"))

model<- glm(trustRatingVis ~  trustRatingData * complexity + 
              trustRatingData *  chartType + 
              trustRatingData *  as.factor(isCovidData),
                      data = results_long_all)
Anova(model)

Analysis of Deviance Table (Type II tests)

Response: trustRatingVis
                                       LR Chisq Df Pr(>Chisq)    
trustRatingData                          384.33  1    < 2e-16 ***
complexity                                 2.39  2    0.30305    
chartType                                  0.20  1    0.65520    
as.factor(isCovidData)                     0.05  1    0.81628    
trustRatingData:complexity                10.69  2    0.00477 ** 
trustRatingData:chartType                  4.61  1    0.03172 *  
trustRatingData:as.factor(isCovidData)     2.16  1    0.14147    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

results_long_all %>%
  # filter(trustItemVis == "bar.vis_2") %>%
  ggplot(aes(x = trustRatingVis, y = trustRatingData, color = as.factor(isCovidData))) +
  geom_jitter(alpha = 0.25) +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth", color = "black") +
  labs(title = "Relationship bewteen trust in Vis and Data") + 
  facet_grid(rows = vars(chartType), cols = vars(complexity)) +
  theme_minimal()

Does the trust items predict trust?

model <- lm(formula = vis.trust_6 ~ vis.trust_1 * as.factor(isCovidData) + 
              vis.trust_2 * as.factor(isCovidData) + 
              vis.trust_3 * as.factor(isCovidData) + 
              vis.trust_4 * as.factor(isCovidData) + 
              vis.trust_5 * as.factor(isCovidData) + 
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)

Analysis of Variance Table

Response: vis.trust_6
                                    Df Sum Sq Mean Sq  F value    Pr(>F)    
vis.trust_1                          1 152.28 152.282 173.1599 < 2.2e-16 ***
as.factor(isCovidData)               1   1.14   1.136   1.2912  0.256397    
vis.trust_2                          1  49.69  49.689  56.5015 2.811e-13 ***
vis.trust_3                          1  60.25  60.245  68.5046 1.285e-15 ***
vis.trust_4                          1   2.44   2.439   2.7733  0.096505 .  
vis.trust_5                          1   0.33   0.327   0.3722  0.542080    
Age                                  1   1.53   1.532   1.7422  0.187499    
Gender                               1   0.84   0.837   0.9522  0.329650    
State_1                             45  71.40   1.587   1.8041  0.001543 ** 
Education                            1   0.00   0.000   0.0001  0.990991    
Parents_education                    1   0.59   0.592   0.6729  0.412463    
Language                             1   0.05   0.055   0.0625  0.802771    
Ethnicity                            1   0.10   0.104   0.1183  0.731013    
Income                               1   0.33   0.331   0.3769  0.539547    
Religion                             1   0.51   0.511   0.5810  0.446288    
trust.in.science_7                   1  36.57  36.570  41.5838 2.778e-10 ***
need_for_cognition                   1   0.93   0.927   1.0544  0.305013    
interpersonal.trust_1                1   4.62   4.624   5.2577  0.022285 *  
vis.trust_1:as.factor(isCovidData)   1   0.01   0.007   0.0079  0.929196    
as.factor(isCovidData):vis.trust_2   1   0.01   0.013   0.0146  0.903748    
as.factor(isCovidData):vis.trust_3   1   0.34   0.342   0.3889  0.533154    
as.factor(isCovidData):vis.trust_4   1   0.34   0.345   0.3922  0.531436    
as.factor(isCovidData):vis.trust_5   1   0.97   0.969   1.1015  0.294479    
Residuals                          476 418.61   0.879                       
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

summary(model)

Call:
lm(formula = vis.trust_6 ~ vis.trust_1 * as.factor(isCovidData) + 
    vis.trust_2 * as.factor(isCovidData) + vis.trust_3 * as.factor(isCovidData) + 
    vis.trust_4 * as.factor(isCovidData) + vis.trust_5 * as.factor(isCovidData) + 
    Age + Gender + State_1 + Education + Parents_education + 
    Language + Ethnicity + Income + Religion + trust.in.science_7 + 
    need_for_cognition + interpersonal.trust_1, data = results)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8773 -0.5207  0.0217  0.5582  3.2650 

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                         -9.9540917  5.7229385  -1.739 0.082623 .  
vis.trust_1                          0.2119431  0.0540206   3.923 0.000100 ***
as.factor(isCovidData)1              0.0416696  0.4449709   0.094 0.925430    
vis.trust_2                          0.2027925  0.0534737   3.792 0.000168 ***
vis.trust_3                          0.2841009  0.0516755   5.498 6.28e-08 ***
vis.trust_4                          0.0098439  0.0284399   0.346 0.729398    
vis.trust_5                         -0.0007802  0.0292475  -0.027 0.978730    
Age                                  0.0051125  0.0028779   1.776 0.076298 .  
Gender                               0.0171607  0.0792673   0.216 0.828697    
State_1Arizona                       0.3335349  0.3921608   0.851 0.395472    
State_1Arkansas                      0.4143523  0.5351867   0.774 0.439185    
State_1California                    0.0756582  0.2728692   0.277 0.781694    
State_1Colorado                      0.0071308  0.3811767   0.019 0.985082    
State_1Connecticut                  -0.3716075  0.4979212  -0.746 0.455844    
State_1Delaware                      0.3293908  0.7210338   0.457 0.648001    
State_1Florida                       0.1532619  0.2918343   0.525 0.599711    
State_1Georgia                      -0.1146918  0.3088288  -0.371 0.710522    
State_1Hawaii                        1.3559094  0.6190836   2.190 0.028995 *  
State_1Illinois                      0.3475432  0.3257636   1.067 0.286577    
State_1Indiana                      -0.5470888  0.4056802  -1.349 0.178116    
State_1Iowa                         -0.2409988  0.5379717  -0.448 0.654374    
State_1Kansas                        0.2236365  0.4326656   0.517 0.605480    
State_1Kentucky                     -0.3856816  0.4049927  -0.952 0.341419    
State_1Louisiana                    -0.1350212  0.4630317  -0.292 0.770718    
State_1Maine                         0.5136628  0.6054283   0.848 0.396626    
State_1Maryland                      0.2699705  0.3553763   0.760 0.447825    
State_1Massachusetts                 0.0410363  0.3556953   0.115 0.908201    
State_1Michigan                      0.1170436  0.3424409   0.342 0.732658    
State_1Minnesota                    -0.1211012  0.4929360  -0.246 0.806041    
State_1Mississippi                   0.5909542  0.5382926   1.098 0.272833    
State_1Missouri                     -0.0241133  0.4011217  -0.060 0.952090    
State_1Montana                       0.3422605  0.7185478   0.476 0.634063    
State_1Nebraska                      0.1604793  0.6037312   0.266 0.790499    
State_1Nevada                       -0.2061880  0.4609322  -0.447 0.654842    
State_1New Hampshire                 2.5055544  1.0295460   2.434 0.015314 *  
State_1New Jersey                    0.0731465  0.4212710   0.174 0.862228    
State_1New York                      0.3379607  0.3064186   1.103 0.270612    
State_1North Carolina               -0.0027368  0.3290524  -0.008 0.993367    
State_1Ohio                          0.0397806  0.3519538   0.113 0.910056    
State_1Oklahoma                      0.2854305  0.4688140   0.609 0.542924    
State_1Oregon                       -0.1862481  0.4247810  -0.438 0.661254    
State_1Pennsylvania                  0.0385778  0.2984822   0.129 0.897217    
State_1Rhode Island                 -0.0301492  0.6048199  -0.050 0.960264    
State_1South Carolina               -0.6933861  0.4936043  -1.405 0.160751    
State_1South Dakota                 -3.7459717  1.0157087  -3.688 0.000252 ***
State_1Tennessee                     0.5876792  0.3706110   1.586 0.113471    
State_1Texas                        -0.2414368  0.2876971  -0.839 0.401776    
State_1Utah                         -0.2168607  0.4630074  -0.468 0.639732    
State_1Vermont                      -1.0766183  0.7223418  -1.490 0.136767    
State_1Virginia                      0.0094756  0.4237948   0.022 0.982171    
State_1Washington                    0.2294027  0.3370422   0.681 0.496433    
State_1West Virginia                -0.3015064  0.6012565  -0.501 0.616279    
State_1Wisconsin                     0.0739541  0.3321842   0.223 0.823919    
State_1Wyoming                       0.0141456  0.9818235   0.014 0.988511    
Education                           -0.0214295  0.0257607  -0.832 0.405901    
Parents_education                    0.0120379  0.0592115   0.203 0.838985    
Language                             0.0684564  0.1661255   0.412 0.680469    
Ethnicity                           -0.0009461  0.0235988  -0.040 0.968037    
Income                              -0.0014227  0.0093345  -0.152 0.878922    
Religion                            -0.0241681  0.0444115  -0.544 0.586569    
trust.in.science_7                   0.1292041  0.0223979   5.769 1.44e-08 ***
need_for_cognition                  -0.0914597  0.0716736  -1.276 0.202557    
interpersonal.trust_1                0.0803311  0.0342667   2.344 0.019474 *  
vis.trust_1:as.factor(isCovidData)1  0.0024529  0.0729555   0.034 0.973193    
as.factor(isCovidData)1:vis.trust_2  0.0008529  0.0787182   0.011 0.991360    
as.factor(isCovidData)1:vis.trust_3 -0.0592930  0.0736064  -0.806 0.420909    
as.factor(isCovidData)1:vis.trust_4  0.0102089  0.0432103   0.236 0.813332    
as.factor(isCovidData)1:vis.trust_5  0.0496181  0.0472776   1.050 0.294479    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9378 on 476 degrees of freedom
Multiple R-squared:  0.4793,    Adjusted R-squared:  0.406 
F-statistic: 6.539 on 67 and 476 DF,  p-value: < 2.2e-16

# 4 and 5 has to do with whether the vis is actionable. Would it be used in daily life? Really depends on the topic - crop stuff might not be relevant. Need to account for the topic!!!!! Add "isCovidData" to model. 

model <- lm(formula = data.trust_6 ~ data.trust_1 * as.factor(isCovidData) + 
              data.trust_2 * as.factor(isCovidData) + 
              data.trust_3 * as.factor(isCovidData) + 
              data.trust_4 * as.factor(isCovidData) + 
              data.trust_5 * as.factor(isCovidData) +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
summary(model)

Call:
lm(formula = data.trust_6 ~ data.trust_1 * as.factor(isCovidData) + 
    data.trust_2 * as.factor(isCovidData) + data.trust_3 * as.factor(isCovidData) + 
    data.trust_4 * as.factor(isCovidData) + data.trust_5 * as.factor(isCovidData) + 
    Age + Gender + State_1 + Education + Parents_education + 
    Language + Ethnicity + Income + Religion + trust.in.science_7 + 
    need_for_cognition + interpersonal.trust_1, data = results)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.8615 -0.4775 -0.0005  0.4795  3.9556 

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          -5.058251   6.120690  -0.826 0.408981    
data.trust_1                          0.331461   0.084381   3.928 9.83e-05 ***
as.factor(isCovidData)1               0.225129   0.496918   0.453 0.650719    
data.trust_2                          0.070922   0.052269   1.357 0.175464    
data.trust_3                          0.242575   0.066234   3.662 0.000278 ***
data.trust_4                          0.087374   0.061086   1.430 0.153272    
data.trust_5                          0.036810   0.056613   0.650 0.515873    
Age                                   0.002485   0.003084   0.806 0.420849    
Gender                               -0.075322   0.083717  -0.900 0.368724    
State_1Arizona                        0.396862   0.412283   0.963 0.336239    
State_1Arkansas                       0.076994   0.569144   0.135 0.892447    
State_1California                     0.180248   0.287912   0.626 0.531582    
State_1Colorado                       0.024541   0.399598   0.061 0.951056    
State_1Connecticut                   -0.587935   0.533696  -1.102 0.271180    
State_1Delaware                       0.211162   0.763313   0.277 0.782178    
State_1Florida                        0.267887   0.308725   0.868 0.385984    
State_1Georgia                       -0.024771   0.325332  -0.076 0.939339    
State_1Hawaii                        -0.765700   0.659989  -1.160 0.246561    
State_1Illinois                      -0.171894   0.346662  -0.496 0.620226    
State_1Indiana                       -1.322666   0.431993  -3.062 0.002325 ** 
State_1Iowa                          -0.780953   0.569821  -1.371 0.171170    
State_1Kansas                         0.091428   0.457726   0.200 0.841765    
State_1Kentucky                      -0.331683   0.429805  -0.772 0.440672    
State_1Louisiana                     -0.142803   0.491179  -0.291 0.771380    
State_1Maine                          0.360595   0.634575   0.568 0.570136    
State_1Maryland                       0.611578   0.376135   1.626 0.104622    
State_1Massachusetts                  0.358868   0.375345   0.956 0.339506    
State_1Michigan                       0.160455   0.360479   0.445 0.656439    
State_1Minnesota                      0.411557   0.518894   0.793 0.428090    
State_1Mississippi                    0.147402   0.573351   0.257 0.797222    
State_1Missouri                       0.644384   0.425346   1.515 0.130445    
State_1Montana                       -0.371953   0.764917  -0.486 0.627003    
State_1Nebraska                       0.471768   0.637478   0.740 0.459632    
State_1Nevada                         0.201743   0.488211   0.413 0.679625    
State_1New Hampshire                 -1.527282   1.061877  -1.438 0.151011    
State_1New Jersey                    -0.188256   0.443312  -0.425 0.671278    
State_1New York                       0.405115   0.320473   1.264 0.206807    
State_1North Carolina                 0.041771   0.342995   0.122 0.903123    
State_1Ohio                           0.017986   0.365622   0.049 0.960787    
State_1Oklahoma                       0.427856   0.491851   0.870 0.384800    
State_1Oregon                         0.239479   0.446380   0.536 0.591870    
State_1Pennsylvania                   0.402393   0.314721   1.279 0.201671    
State_1Rhode Island                  -0.268033   0.639979  -0.419 0.675540    
State_1South Carolina                 1.012178   0.517679   1.955 0.051142 .  
State_1South Dakota                  -1.810325   1.060051  -1.708 0.088331 .  
State_1Tennessee                     -0.729500   0.387059  -1.885 0.060076 .  
State_1Texas                         -0.194173   0.300807  -0.646 0.518910    
State_1Utah                           0.042312   0.487196   0.087 0.930829    
State_1Vermont                       -0.500535   0.758303  -0.660 0.509526    
State_1Virginia                       0.324863   0.445986   0.728 0.466717    
State_1Washington                     0.059798   0.350307   0.171 0.864532    
State_1West Virginia                 -0.155004   0.634192  -0.244 0.807017    
State_1Wisconsin                      0.529684   0.351640   1.506 0.132647    
State_1Wyoming                       -0.708338   1.038761  -0.682 0.495630    
Education                            -0.038483   0.027106  -1.420 0.156345    
Parents_education                     0.046412   0.062788   0.739 0.460159    
Language                             -0.025552   0.176124  -0.145 0.884710    
Ethnicity                            -0.024519   0.025103  -0.977 0.329197    
Income                                0.005705   0.009899   0.576 0.564661    
Religion                              0.016868   0.046885   0.360 0.719167    
trust.in.science_7                    0.067788   0.024246   2.796 0.005386 ** 
need_for_cognition                    0.122462   0.074268   1.649 0.099822 .  
interpersonal.trust_1                 0.104217   0.036695   2.840 0.004703 ** 
data.trust_1:as.factor(isCovidData)1 -0.385037   0.106591  -3.612 0.000336 ***
as.factor(isCovidData)1:data.trust_2 -0.011603   0.068013  -0.171 0.864616    
as.factor(isCovidData)1:data.trust_3  0.129051   0.089100   1.448 0.148169    
as.factor(isCovidData)1:data.trust_4  0.156862   0.093308   1.681 0.093394 .  
as.factor(isCovidData)1:data.trust_5  0.025248   0.087352   0.289 0.772680    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9921 on 476 degrees of freedom
Multiple R-squared:  0.4826,    Adjusted R-squared:  0.4098 
F-statistic: 6.626 on 67 and 476 DF,  p-value: < 2.2e-16

#anova(model)

results %>%
  ggplot(aes(x = data.trust_1, y = data.trust_6, color = as.factor(isCovidData))) +
  geom_jitter() +
  geom_smooth(method="lm") +
  facet_wrap(~isCovidData) +
  theme_minimal()

How does performance on VLAT questions predict trust?

model <- lm(formula = vis.trust_6 ~ vlat_simple * vlat_moderate * vlat_complex +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)

Analysis of Variance Table

Response: vis.trust_6
                                        Df Sum Sq Mean Sq F value    Pr(>F)    
vlat_simple                              1   1.64   1.637  1.3161  0.251865    
vlat_moderate                            1   0.93   0.934  0.7509  0.386634    
vlat_complex                             1   3.49   3.489  2.8057  0.094582 .  
Age                                      1   2.23   2.231  1.7939  0.181089    
Gender                                   1   4.30   4.298  3.4556  0.063651 .  
State_1                                 45  83.54   1.856  1.4927  0.023955 *  
Education                                1   0.01   0.010  0.0078  0.929451    
Parents_education                        1   2.89   2.888  2.3226  0.128168    
Language                                 1   0.17   0.170  0.1364  0.712066    
Ethnicity                                1   2.14   2.136  1.7173  0.190670    
Income                                   1   0.04   0.042  0.0334  0.855019    
Religion                                 1   0.16   0.164  0.1321  0.716472    
trust.in.science_7                       1  80.19  80.192 64.4809 7.585e-15 ***
need_for_cognition                       1   4.52   4.523  3.6369  0.057110 .  
interpersonal.trust_1                    1   8.55   8.554  6.8782  0.009003 ** 
vlat_simple:vlat_moderate                1   4.73   4.731  3.8043  0.051702 .  
vlat_simple:vlat_complex                 1   7.04   7.043  5.6631  0.017715 *  
vlat_moderate:vlat_complex               1   0.27   0.269  0.2160  0.642280    
vlat_simple:vlat_moderate:vlat_complex   1   0.08   0.085  0.0681  0.794193    
Residuals                              480 596.95   1.244                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

emmeans(aov(vis.trust_6 ~ vlat_simple * vlat_complex , data = results) , ~ vlat_simple | vlat_complex)

vlat_complex = 0:
 vlat_simple emmean     SE  df lower.CL upper.CL
        0.77   4.43 0.0874 540     4.26     4.60

vlat_complex = 1:
 vlat_simple emmean     SE  df lower.CL upper.CL
        0.77   4.59 0.0664 540     4.46     4.72

Confidence level used: 0.95 

results %>%
  gather(key = vlat_level, value = vlat_performance, vlat_simple,  vlat_moderate, vlat_complex) %>%
  ggplot(aes(x = vis.trust_6, y = vlat_performance, colour = as.factor(assigned_vlat))) +
  geom_jitter(width = 0.5) +
  # y(0, 2) + 
  # labs(title = "Trust in data") + 
  # geom_vline(data = results %>% 
  #              group_by(complexity, isCovidData) %>%
  #              summarize(n = n(), 
  #                        vis.trust_6 = mean(vis.trust_6)), 
  #            aes(xintercept = vis.trust_6, colour = as.factor(isCovidData))) +
  facet_grid(rows = vars(vlat_level), cols = vars(chartType)) +
  theme_minimal() + 
  theme(panel.spacing = unit(2, "lines"))

        # legend.position = "none")

Overall distribution of VLAT performance

Trust in Vis

vlat_long <- results %>%
  gather(key = vlat_level, value = vlat_performance, vlat_simple,  vlat_moderate, vlat_complex) 

model <- lmer(formula = vlat_performance ~ vlat_level * isCovidData * chartType +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1 + (1|ResponseId),
            data = vlat_long)
Anova(model)

Analysis of Deviance Table (Type II Wald chisquare tests)

Response: vlat_performance
                                    Chisq Df Pr(>Chisq)    
vlat_level                       190.3235  2  < 2.2e-16 ***
isCovidData                        3.9830  1   0.045960 *  
chartType                        113.8575  1  < 2.2e-16 ***
Age                                6.7588  1   0.009329 ** 
Gender                             2.2814  1   0.130931    
State_1                           38.5402 45   0.740639    
Education                          4.3702  1   0.036574 *  
Parents_education                  0.0646  1   0.799389    
Language                           1.3871  1   0.238904    
Ethnicity                          0.2042  1   0.651378    
Income                             0.0468  1   0.828744    
Religion                           0.1139  1   0.735744    
trust.in.science_7                 2.5459  1   0.110578    
need_for_cognition                 1.2910  1   0.255860    
interpersonal.trust_1              0.3739  1   0.540882    
vlat_level:isCovidData             6.4935  2   0.038900 *  
vlat_level:chartType             145.9039  2  < 2.2e-16 ***
isCovidData:chartType              0.6755  1   0.411131    
vlat_level:isCovidData:chartType   0.3441  2   0.841952    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

emmeans(aov(vlat_performance ~ vlat_level , data = results) , ~ vlat_simple | vlat_complex)

Error in eval(predvars, data, env) : object 'vlat_performance' not found

```r
results %>%
  gather(key = vlat_level, value = vlat_performance, vlat_simple,  vlat_moderate, vlat_complex) %>%
  group_by(vlat_level, chartType, isCovidData) %>%
  summarize(n = n(),
            mean_vlat_performance = mean(vlat_performance),
            se = sd(vlat_performance)/sqrt(n),
            n = n()) %>%
  ggplot(aes(x = vlat_level, y = mean_vlat_performance, 
             ymax = mean_vlat_performance + se, ymin = mean_vlat_performance - se,
             colour = vlat_level)) +
  geom_point() +
  geom_errorbar() +
  # labs(title = \Trust in data\) + 
  # geom_vline(data = results %>% 
  #              group_by(complexity, isCovidData) %>%
  #              summarize(n = n(), 
  #                        vis.trust_6 = mean(vis.trust_6)), 
  #            aes(xintercept = vis.trust_6, colour = as.factor(isCovidData))) +
  facet_grid(rows = vars(isCovidData)) +
  theme_minimal() + 
  theme(panel.spacing = unit(2, \lines\))

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summarise() has grouped output by ‘vlat_level’, ‘chartType’. You can override using the .groups argument.

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<img 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" />

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```r
```r
        # legend.position = \none\)

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<!-- rnb-text-begin -->



Trust in Data



<!-- rnb-text-end -->


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```r
```r
model <- lm(formula = data.trust_6 ~ vlat_simple * vlat_moderate * vlat_complex +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)

<!-- rnb-source-end -->

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Analysis of Variance Table

Response: data.trust_6 Df Sum Sq Mean Sq F value Pr(>F)
vlat_simple 1 0.00 0.005 0.0033 0.9545472
vlat_moderate 1 0.00 0.002 0.0017 0.9673539
vlat_complex 1 1.01 1.007 0.7029 0.4022243
Age 1 0.52 0.518 0.3613 0.5480605
Gender 1 0.04 0.041 0.0288 0.8653173
State_1 45 128.33 2.852 1.9910 0.0002402 Education 1 0.91 0.906 0.6324 0.4268659
Parents_education 1 4.54 4.537 3.1678 0.0757362 .
Language 1 0.04 0.041 0.0283 0.8665279
Ethnicity 1 0.07 0.068 0.0476 0.8273695
Income 1 4.13 4.127 2.8811 0.0902722 .
Religion 1 1.95 1.954 1.3640 0.2434309
trust.in.science_7 1 51.30 51.299 35.8161 4.247e-09 need_for_cognition 1 6.62 6.619 4.6210 0.0320825 *
interpersonal.trust_1 1 13.35 13.346 9.3176 0.0023955 ** vlat_simple:vlat_moderate 1 0.75 0.750 0.5235 0.4696899
vlat_simple:vlat_complex 1 3.54 3.536 2.4685 0.1168047
vlat_moderate:vlat_complex 1 0.90 0.904 0.6311 0.4273312
vlat_simple:vlat_moderate:vlat_complex 1 0.00 0.002 0.0011 0.9735414
Residuals 480 687.50 1.432
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

<!-- rnb-output-end -->

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```r
```r
emmeans(aov(data.trust_6 ~ vlat_simple * vlat_complex , data = results) , ~ vlat_simple | vlat_complex)

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vlat_complex = 0: vlat_simple emmean SE df lower.CL upper.CL 0.77 4.40 0.0930 540 4.22 4.58

vlat_complex = 1: vlat_simple emmean SE df lower.CL upper.CL 0.77 4.48 0.0707 540 4.34 4.62

Confidence level used: 0.95

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# How does provenance data predict trust?

Overall


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```r
```r
brushed <- results %>%
  gather(key = provenance_type, value = provenance_value, brushed, explore_interactions, explore_time) %>%
  group_by(provenance_type, chartType, isCovidData, complexity) %>%
  summarize(n = n(), 
            mean = mean(provenance_value), 
            se = sd(provenance_value)/sqrt(n),
            n = n) %>%
  filter(provenance_type == \brushed\) %>%
  ggplot(aes(x = provenance_type, y = mean, ymax = mean + se, ymin = mean - se, color = as.factor(isCovidData))) +
  geom_point(position = position_dodge2(width = 0.5)) +
  geom_errorbar(width = 0.5, size = 0.66, position = \dodge\) +
  facet_grid(rows = vars(complexity), cols = vars(isCovidData)) + 
  theme_minimal() +
  theme(panel.spacing = unit(2, \lines\))

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summarise() has grouped output by ‘provenance_type’, ‘chartType’, ‘isCovidData’. You can override using the .groups argument.

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```r
```r
brushed

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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5pbnRlcmFjdGlvbnMgPC0gcmVzdWx0cyAlPiVcbiAgZ2F0aGVyKGtleSA9IHByb3ZlbmFuY2VfdHlwZSwgdmFsdWUgPSBwcm92ZW5hbmNlX3ZhbHVlLCBicnVzaGVkLCBleHBsb3JlX2ludGVyYWN0aW9ucywgZXhwbG9yZV90aW1lKSAlPiVcbiAgZ3JvdXBfYnkocHJvdmVuYW5jZV90eXBlLCBjaGFydFR5cGUsIGlzQ292aWREYXRhLCBjb21wbGV4aXR5KSAlPiVcbiAgc3VtbWFyaXplKG4gPSBuKCksIFxuICAgICAgICAgICAgbWVhbiA9IG1lYW4ocHJvdmVuYW5jZV92YWx1ZSksIFxuICAgICAgICAgICAgc2UgPSBzZChwcm92ZW5hbmNlX3ZhbHVlKS9zcXJ0KG4pLFxuICAgICAgICAgICAgbiA9IG4pICU+JVxuICBmaWx0ZXIocHJvdmVuYW5jZV90eXBlID09IFxcZXhwbG9yZV9pbnRlcmFjdGlvbnNcXCkgJT4lXG4gIGdncGxvdChhZXMoeCA9IHByb3ZlbmFuY2VfdHlwZSwgeSA9IG1lYW4sIHltYXggPSBtZWFuICsgc2UsIHltaW4gPSBtZWFuIC0gc2UsIGNvbG9yID0gYXMuZmFjdG9yKGlzQ292aWREYXRhKSkpICtcbiAgZ2VvbV9wb2ludChwb3NpdGlvbiA9IHBvc2l0aW9uX2RvZGdlMih3aWR0aCA9IDAuNSkpICtcbiAgZ2VvbV9lcnJvcmJhcih3aWR0aCA9IDAuNSwgc2l6ZSA9IDAuNjYsIHBvc2l0aW9uID0gXFxkb2RnZVxcKSArXG4gIGZhY2V0X2dyaWQocm93cyA9IHZhcnMoY29tcGxleGl0eSksIGNvbHMgPSB2YXJzKGNoYXJ0VHlwZSkpICsgXG4gIHRoZW1lX21pbmltYWwoKSArXG4gIHRoZW1lKHBhbmVsLnNwYWNpbmcgPSB1bml0KDIsIFxcbGluZXNcXCkpXG5gYGBcbmBgYCJ9 -->

```r
```r

interactions <- results %>%
  gather(key = provenance_type, value = provenance_value, brushed, explore_interactions, explore_time) %>%
  group_by(provenance_type, chartType, isCovidData, complexity) %>%
  summarize(n = n(), 
            mean = mean(provenance_value), 
            se = sd(provenance_value)/sqrt(n),
            n = n) %>%
  filter(provenance_type == \explore_interactions\) %>%
  ggplot(aes(x = provenance_type, y = mean, ymax = mean + se, ymin = mean - se, color = as.factor(isCovidData))) +
  geom_point(position = position_dodge2(width = 0.5)) +
  geom_errorbar(width = 0.5, size = 0.66, position = \dodge\) +
  facet_grid(rows = vars(complexity), cols = vars(chartType)) + 
  theme_minimal() +
  theme(panel.spacing = unit(2, \lines\))

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiYHN1bW1hcmlzZSgpYCBoYXMgZ3JvdXBlZCBvdXRwdXQgYnkgJ3Byb3ZlbmFuY2VfdHlwZScsICdjaGFydFR5cGUnLCAnaXNDb3ZpZERhdGEnLiBZb3UgY2FuIG92ZXJyaWRlIHVzaW5nIHRoZSBgLmdyb3Vwc2AgYXJndW1lbnQuXG4ifQ== -->

summarise() has grouped output by ‘provenance_type’, ‘chartType’, ‘isCovidData’. You can override using the .groups argument.

<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuaW50ZXJhY3Rpb25zXG5gYGBcbmBgYCJ9 -->

```r
```r
interactions

<!-- rnb-source-end -->

<!-- rnb-plot-begin -->

<img 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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r

exploreTime <- results %>%
  gather(key = provenance_type, value = provenance_value, brushed, explore_interactions, explore_time) %>%
  group_by(provenance_type, chartType, isCovidData, complexity) %>%
  summarize(n = n(), 
            mean = mean(provenance_value), 
            se = sd(provenance_value)/sqrt(n),
            n = n) %>%
  filter(provenance_type == \explore_time\) %>%
  ggplot(aes(x = provenance_type, y = mean, ymax = mean + se, ymin = mean - se, color = as.factor(isCovidData))) +
  geom_point(position = position_dodge2(width = 0.5)) +
  geom_errorbar(width = 0.5, size = 0.66, position = \dodge\) +
  facet_grid(rows = vars(chartType), cols = vars(complexity)) + 
  theme_minimal() +
  theme(panel.spacing = unit(2, \lines\))

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiYHN1bW1hcmlzZSgpYCBoYXMgZ3JvdXBlZCBvdXRwdXQgYnkgJ3Byb3ZlbmFuY2VfdHlwZScsICdjaGFydFR5cGUnLCAnaXNDb3ZpZERhdGEnLiBZb3UgY2FuIG92ZXJyaWRlIHVzaW5nIHRoZSBgLmdyb3Vwc2AgYXJndW1lbnQuXG4ifQ== -->

summarise() has grouped output by ‘provenance_type’, ‘chartType’, ‘isCovidData’. You can override using the .groups argument.

<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuZXhwbG9yZVRpbWVcbmBgYFxuYGBgIn0= -->

```r
```r
exploreTime

<!-- rnb-source-end -->

<!-- rnb-plot-begin -->

<img 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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuXG5leHBsb3JlVGltZSAvIGludGVyYWN0aW9ucyAvIGJydXNoZWRcbmdnc2F2ZShcXHByb3ZlbmFuY2VSZXN1bHRzLnBuZ1xcLCB3aWR0aCA9IDE3LCBoZWlnaHQgPSAxNylcbmBgYFxuYGBgIn0= -->

```r
```r

exploreTime / interactions / brushed
ggsave(\provenanceResults.png\, width = 17, height = 17)

<!-- rnb-source-end -->

<!-- rnb-plot-begin -->

<img 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nQ1Q1+46G2xy1tttVXsJq2XQQKJ2hptKFE7SnRMND4UnHjKLmliP7LUFqPJZddyojZQ0FomaovR+WDZlbIbTUTLIlC2CMjCW4auNwPOUCYUf7IMXXRVVQSymICeaVl8cVU1EUgzgYxQeBn9z0ApJkNw8RIjgwnofuKBFi1h9AC6VAl5hA/gnnvumcf/iy5V8mvkQtm4WIzRh2tZBERABERABERABESgjBEocYWX6UkZwEC0ACIHTJkyxYeooYtq1KhRPkpAtEWSSAEMaGHgTKtWrfysOaRlgBfKLyO+mYGM2IzE+iQUjQtyXsYuq6orAiIgAiIgAiIgAiIQEihxhZcR3Qx8QYFFUFaZ5IAIAkQhQJl1MzmF5fW/zz//vI9SwBSnCJELCGaP9Ze0RDkgrBZT7RLtgGksw0D5/gD9JwIiIAIiIAIiIAIiUGYIlPigNZRVRs9+/fXX3qWBkdtYcBEC1rdu3dpuuukm7+qApRYr7qxZs2z//ff34bRY33XXXb1lmBiLxIatX7++jzGLSwORCJi9jMDj8YTR4rhUZLNQxwl/Lrdf165z/Iqmps22rGo7u0kzSlr4sKFNZJoQDMVNA51pxUpreagjE1YwMUomXoN0VlbtLJ00C5ZX2M4Y+Li50R0KdsaSS02knER1JMJFGLM6XgkxFiUa+BwvvbaJQLYTKHGFl9BboUsDAcHx2cXKi+DegJ+um1/dsAQT85WpKBcsWOADh+++++5+mRuf5d9++81bhLEW49LAlLy8mDhHIoWXFzRKdjYLL4iBc+ba278vK7Jqnl2/rp2/Xf0iyz/VjIkQkInKVhiGK9V6lMZ0tDPqyQs6E69BOpmiSETPMpfOvAubV1l4ntHOaGOJlMHCMsyU44kqkah38umnn/ZxjDHw1KtXz3788Uf/sRmOW+GdePfdd2dKVVQOEShxAiWu8DJr12GHHeZDbWHlZbamr776yrs0cLPfeuutfhIELLdMRUoQeyZdIOA9MRgRJk9gG5YlppZ8+OGHvUtDnz59/GxOyV5MPDCTxaMttiv02admixYUyek2bNhoF7dqbefvuXvSFwQKy5Hfjbe/XPoN/y1J0yqVrX+bVvmWq7oL+VOlQok3p4xVtHg5Z0Q7y/dKbn4CPh4/WrjIPl+1xrWzorGy71ptKzu6bp3NL2SajsxkRSvb2xnPKQY0Y8Hc3Mkq0tQMijybZO2MnlFmLLz55pt9OWDCDHaXXnqpnximyAtXjCfA2PXee+/5iWxuu+22Qp95xYoVVq1atULnw4yj9FLzwYGuAv9ExrXYk/G8xKA3evRob9xjJkBcMqPHLMUeU5D1iRMn2vvvv29XXnll3MNw+XzyySftsssusy1d7yx6E8bDUIiPzYyZRxxxRMKPrjBt7C+9mXyUxoaYjE3HOukuuugiu/fee1NKHy+PVLeVuIZCY2EGL+ZTx4rLrGK4NDCADUsu7goI4FFoJ0+ebDzwaGChEMsRwFwg4IVfuHXq1PFpw3SJfsm3pGXDxB+dSTtvRIp0lYna1apdxyq33TWpwvvoz3NsjVN2N0adeP7fa+07NwlF73oFm9EtKgst/pdAJrSzor4YU9f8Za8uWlxkp/nL3d/HNCj5noQiq2AaMs72dsbzH0EZzPa6JuspYYpulKxQUFoOPfRQbzBiJsRsESIuYdTq3bu3N34Vtl7M9MiMkaHBbHPzw0CHVR3dBP2DawH3VBRe3Cip04gRI/yAfXSWV155xc+i+uGHH3qXzc0tV3gcCi9KZCKFl4ABuIuig9F2mH0Q/YnZCLnHUIh/+eUX7xaDfsYsmKkISjNp33nnHWvWrFm+h3AfN2rUyCvcicqabyYpJihxhZdICnzhPPHEEzZgwADvg3vIIYf4Lw3gv/DCC96aO2/ePH8xateu7bsSJ0yY4KM4EJ5s4cKF3m8X1wW++F999VWvEAOedb6EEwlfWViHS1py9+9qOXu2z7cYld4darl/LDM32tCnXbdHO1vfepd8j9tYr77xVZtMfnS8opVd0qJgTFm6zP6sXDpmXOMjJ9lLIln9i3IfD5BMaGdFWUfyPmibra2Z6xVIJmtdm7ripzl5kjCn2tXbN7C6lSrm2R67Utvdz5nAkRkLk1nfYstdXOtlpZ39sGq1vRzThhIx3uDuvdXuOV/B9TpUzk3NuNFoi0p2rnPTKmnBdSaRUr/ffvv5Kbqx6LZo0cK+/fZbP9CbSEbZJLg5MvkM7/V03HN8KBx55JGFRsQU6A888IDPB7eTgowFOvHEE+2bb77xCiUuKaFgIT799NMNZTXeLJZhulR+6RHnryBywgknWLQFnRCvfERR3h9++MErxvnlx/N5+vTp+SXLsx+3VqJuUXemAi8qKXGF99lnn/VfEkBFUFZQfI877jiv2KLw0nWFYovySiPAkotrAxcGpZguPBRhHgz8Pffcc34bVmK6LZKZ1XF3IL8Sl+pt8i3CxiGD3TyrTmn9r7LLARV/mGCVuh1kOY0aJzyeGxEWPDSSPTCaOlaf/bnC1kblz+uhseOTEYwS1vB/OzJR2aV0cM8EhhvfHWJuhOj/gCVb+tsNHnUWfyvvWkGl/D94+ARr0qGjNT/08KT+rU/OmWcV3X2+Lqqd8aH1g7vHz9hpx2Qlyph9ye6jkiwk7T8T2llRMkCpX7TsDxv5R9EZKvaoXi0jOCZzx6MbmN5NlC7cALFaXnjhhWmxDhb2+jEehy7yadOm+XcxFr+LL7440jVO+FD2z50715o0aWJnnnmmj84Ue9633nrL6wMYprBEMj6HvN59910/pmfOnDnWoEED69u3r2EoCwVF7ZlnnvED21GkjjnmGO8ued9993klc+jQoV5XuOaaa/xAW/yhP/jgA7/cpUsXzzF0l6GcWCpHjhzpdRXGGNHTjAWUOQAQjGrnnnuudw/A3RKl75577vEfIVyjAw880Ctz3J+/OKspFlMsoNHKLvlwLa+99lqfhnJT70Rlw8LMmCbKF/1RRPQq3DnZhtUYyy2CFZrIV1iQOS+RsPITxsRQTnrU4RlGxkp0fdE1YIpgQee6Unf0t4ceesjzwPCGRRtXih122MGnZQ4GuN9yyy2R8vodaf6vxBVevtoIKYb7AheEi4GCSwMCHj4tdGWguBKlgQvFyFTCjuG7BCi6BgDKlwH7aDT4wbCNhp5sqlMU5mQjXdPMu1DZlXdffbmOSbQE7gZa776mNtb9n4tH9H6WuWmwqq1bvSap9bOva9xPOGUkxx2D8oKyW83dNAdvXd1+dxaVZFLRWU8qOqWupIWPm0xUenlJZ0I7K7d8hZVbtrRgl2mtS+584/MT2k2Oc0fiPk7Wq7LK3Zfro5TdMN+Vrm1nAqOwPMl+eUZlqpQWhpvLj3upg/PlHtRy5zwv+tj8Vrp3yFlTZ8RuthPr1rbDtq21yfboDVXK5WZEW0TpilZmosvIPcZ+FJfXXnvND9pOZtyJPrYolzFGMYHU0UcfbSeddJLv9keRWbJkiR9Eh6KIcoP1EYXok08+8d36WDVRFqNl++23t0aNGvnB7IQr5V3+2GOP2dVXX21XXXWVzweFDIMZFlMUUMbxoPxi5MHHmahOxPmfOnWqzx+jGUpyy5Yt/alQolGAzzrrLG/BvPPOO2348OFeF+FdgtsBrgGUA+WV7n8U1mgrMXoECiGGOuqARZQeZ/JE+b7iiit8/VEGUVS5piiCsYIO89RTT0U2Jysbrgcow5SDQfrImDFjPB/OQ+85CnGo8GJF5UOBHoEZM2YY1txUhA8p6s71QZJdX64zuhntkWADHIt0797d9zDDg+cTrPDJJhJXaDzAV5jrFZbXH5jm/0pc4aVh0UDffPNN7xzNTczLJLS88oWIAosVl8bGH0oujQmHb5RaXrChksMvF2bp0qXeLYKGRcNPJokeKMmOKYl9OU7pDxbM9wpp5PzuRst1N2COq2ci4QVx17z5KUdp+I+zhFOUXYZ/OGW5/TffJco6sv38htvZRTtsH1kvqYWwHZTU+ZOdNyPa2bF9bYP7SyY5H420nGHvWI5rW6EErssuOKq3BZ33Djdt8stDf517mFVyHz7J6nqgUzYe/3XBJsf3cu4oyY7b5ABtiEsgIxhOmWw5342LW77Cbwxs2/oNrPZ++yftSXjntyW2pXsurnLPsGj5esVKu76U9CQke56h+I127oAYNHgfYhEkBj0TNoWKRnS9i2uZ7uxjjz3WK2MoMyhWKLsoegguCryTMVjRM0taXDJ4T8UKCuy+++7r64RyjAwaNMiw1KI8ISjOuBiRP+npIUapRtENI1ygI6AAkgd+q4QwRVHEDWTgwIFe4UXhQlCWUa5Rgnv27Om3oXPANby3sPB2cUp7Ivnyyy+9P26/fv18EpRgrhPy/fffG4p8fh8nqZStR48enkeo8KLgUn5c+6IFX2O4wD5U9GkvWFlTESJ+oCQjya4vuhtKP1ZqriuWcXQxrjNuq1xnBAsvHym0i7CspMU9FVdU9LyikBJXeGlsVDq6sWGxpfsDCy+NGIgotuFANZRifHqjXRpoTIQlo2E+7yamQEHmpgIcD4NEjuTcZORdGiTnyKNsi+nT/IMBa1rgHiZWrrz97QajuUokrAJ1xPexaWVn/UyYqnA7qrsPjUzgSF2TvSQKV8vNP7o0tbOKv86z8lHKLrXOwWo7f579k087e9YNWHtk/qLNAnXxNPdAnZb80B41t7HbdmyUPFEx7C2sf11RFjET7sPy8+ZaxW/HFlk1y+3S1tZ22juiRMQ90foNcZ93OU6vygRGccscs5H3WSL54osvvKWMdx+KFT6lKCPDhg3zEy4lOq6otx988MFeGcRyi1WV8KIom6ES3qFDBz8waqeddvKWSRQ0fFdT9d1EUUYxwt0Bgxi+pQx0D3s2cJdg8Fio7FJfPg7iCWnRLw444IDIbpRhyorCGSq8RI8KlV30D5Q/lNZEQs801koUUBQ7lGsUTIReanQVDAShdTNePqmUjd5x3DnQdSgflmcU21hhzBOKZajssp/rlKrCixtC6CqV3/WNPTfXlXJRBnQz2uhnn33mk4VzLrAS8uRjYu+9ExtWYvMvyHqJK7y4NOATw1cANwY3axguBFB33HGHV1YJucKXHOZ04kxy02C254HAlwQ+M1xwGj0+LfiG8FBjXzI/KBpcRoTxefpJ97LP523PlXU3p6ukv8ZeeXWuBFWuvzbpNQ+cg8IFPXraZfvul/QGS5pJKdmZicou6ChXRrSzVK5jg+3MiBoS7T6Tk2sV3PYK7kMykfDireoGBdV11mBX3Xxlg1M8cG0o59KWT+UAl2NN99GWCRyTvajyrXgRJ8gEPrZXZ7OdmiWvqbv29uhDxCXKMy7BzjrHrErVhMfy8ThrY2DfLPvPMz9RQpy/YpsV/WCd3MDKt/5IHnu9dsUKdlA+bg+JzpvO7cnaGb6vjLSnC3mli6TD/ccgNyZaKknBmkgXNgoniifuBLy/UXQQXAJwP8BSS7c2iiHd7EOGDEnJ/5gub1wa6DInb1we8WEOBesukRNSEfQGwp9Gf8DyrMYiCc9QopVxdAyMcckstPRKo0QTTg195LrrrvMuGLhLYC1FQf3F+fJyDaOFto2rBkplKmUjHTyxRsMbHSieby55oWBHG4SS6UWxZeLa4VKK5Hd9o49lGV58OGD17tSpk79mWP1ZjxbKD3vaclFJiSu8NCqmBOYriNnTQqFBhGHJgECj5GJGhyVjEBaCFZcuC75AuKiYy8PGyAXOT8i3pGUD1toUlASfKMlXf/x6uIzdP+qZ7AEa/1htTReBTGhnG197xYLPRudfpaiHvU8cOKXkzdf/85fgaO6ik1w38+lH9076kZngcG1OE4FMaGfO2dLFQkzuJ7vRtUP/dI5+RvMcdBa7cqeenpAGz/TJP/9it/z0S8I0iXagwrwwf2Gi3ZHtDFo7JAPiPfPuSyT4idJFT9c+Ctujjz5qxLVHeSxJwWWALnysumFbxBodKpBYR7Geoujyh/WQLnnKH60DxKsDFkEUQnxqGaCHkC++wrz7EZTI0N/Ub3D/3X///V7JjA1FxlgfFGSsj2EIVKzHKHV0y8cTFEyOQwmMtgyHadFd8GFF8eSPcuHbSo80bFB4ccFgGwPso4UQrQx2w6KcStlQWrGOcz6WaRPhYLvofDknVmXqudtuu/ldH3/8cXSShMt8mNCjTt4IdUh2fcM2G+pefMhwLga6hVZctiHhNWMZf16OCcvHtnRL+XRnWND86E4I3Rmij8WPAxiEEEEhpmsEGHx5cWFpkHw58OVIA6WbANcFLjaO2QxWw2zOOl0QiYSbha+fEpc+fYu8CPmFJSvyAhTDCRSWLDnkiu4eqlA5sZU2+uicdc5NhpeI+1AK3H2U0heZS8c9me2isGSFv8IV3Qu4omtfeVQ6t75+yW+2Mp9n8s4u9N0lDeoVvhAJcsDCmwnvhWRhyegux5KJ6wDPPeLV8i4NLXEJqlbkm3EHmD17tnczQBFnUBljdHBhCKVfv37elYCy8l7ifY+vLoKbAhZLojJg6IoW3v1YW7Fsox+gAzAgjN5clpHTXWgr3BKwsvIxgCKFcknEAoTj0SfQG3CnoDeYXmb8gukxRtFFzwjL4w+K+Q8FMrRYx+zyeTDLK0o+eaKD4LbJQLnQEMeHSbdu3bx1FoWV6wcjlF2s4gz4Q29JpWy4NVAeJNrSHV0ueOBS8e9//9vPd4CuRMSGWEGx/fzzz72uRZkZJ4WFGheNMCJFfteXNovQHknLH3oWHxYovETWwOKNhNeMZXiix5G+qKTEFd5EFaNh0zj4AuLCAJ9GyA2ERRfzN74rWCy5kOzja5LtNBy6EtjH9rCRxTsX5wl9U+Ltz4ZtdL/w1ZlfWLJsqGv4dZlpdaEtZkQ7Y8BaPoPWNpcdD7V1TtnlHuW+ymbJ1J4S2n9GtLMULn7Q3A1U+nRUXtcZd1z51q2T1gHDRyOXrkXj/7wHUjhVqU2S7D7CapmJwsA5BkehxPHuxWKHIodlFuWWwUlYaLG2oviirOLjGlpfsc7isoDRKlbhRXlkuuQbb7zRRzOgZ5dwYAyUwucV4XyMDSJsG26P6AAs0/2P0L3OGCEMYih4GMgoB1ZL8m/t2h8WyXDMkD8o5j8ssMxiFk+4B7FWU1+UXBTXxo0b2+DBgyPJiaGMAk60gq5du3ojAeemHMRRZpm/VMpGuXFrgSO/8YTrgLsoLp5YjhH8d7H4RgsfUfwhPEewlmOZDq3pbM/v+uKSCmvcFkiL0k+0CazhuFtRL9rDOeec469Z6FfMdYdrUYqbv8A9PTJE+LKhGwSrL42ELyBi4/EVEIYlQwHmZunSpYu3/PJFhkM6Xw583YbH0PXBzULj5phEo/44T7ZbPlFEqGfoI5Mhl7tIisE1z0RlBGtEJliMigT6fzOljnxc8UDLxGuQzrrzMgi7a9OZb2Hz4nGOtaykJeen2ZY7dUq+xciZOtly/+tzSohFpyHZxs77JD2OF9Y/Tomx3dtl5DVIWvgC7sRIwf0UT7BcEvIqVlCuitJKFnu+ROuMy+GdE7oexkuHpZZndjKjVLzj2Pbrr7/6eib6KOBewJ8ZpTPWEIKizDsx2t8dqyfpEukK0eXAOoyiiS8yLpSJhN4udBF6hBIJ5UDxxlqf6FoXpGyJzhNux3jINdkc5mEe/OZ3ffHFhW/4nIQ57RUrbqzw3iDMGlMd495aVJKxZhgaMQPT6A4J3RNoiPyhGPN1xS9KBM7POIMnOyYRQI4JTfCJ0pT27Xww8BLkwRI2vtJep9JWfhTAbG9nPLR4oPFxyotOUvwEeGFnQjtb9/WXts6FtyuI+NkjnXJQLoXjAhelodI+++VRWApyrmxIy4DvcLQ7ShOTOKDEEOA/ExTe6IFeiXgXppz5DUzjXkiUhudT7DOqIPcN1l8suFib40VFCOuLvy9/yQQdhI+UZFKQsiXLh33JlO/8jo3en9/1jf3QgXc8ZZc88V2mp74olV3Ok7EWXgqHrwdzK6OkoTDgZ0N3CDc3oUnoNmE73RkoxcmO8TvL8H987cZ+5ZZhHKp6ERFQOysisKUs28BZ+53jXpGVGmtwbpa7zWwOPKxq9JDGKnObk5eOSU4AQxIuBPhQY0WWbB4B3hm4oaD0hu4Nm5dT/kdllMKbqLhYcbFOxgo3Ny4NfCHFSqJjYtNpXQREQAREQAREQAQKSgAdhI8LPjIkm0cgdPdLpxU7UUnSqvDi18KsaWjs0RI9OjN6u5ZFQAREQAREQAREQAREoKgJbGoa3YwzougSRw8Xg3gSqwDHS6NtIiACIiACIiACIiACIlAUBNKi8BK2Ar/aDz/80Bo2bJj1I7SL4kIoTxEQAREQAREQAREQgaIhkBaFl/AoF1xwgQ8JVjTFVK4iIAIiIAIiIAIiIAIisHkE3DyOhRdG2DFVoEQEREAEREAEREAEREAEMo1AWgatMUcy8dP69Olj7du39zMtRVc0v/mxo9NqWQREQAREQAREQAREQATSSSAtCi/zVT/99NMJy6VBawnRaIcIiIAIiIAIiIAIiEARE0iLwssMS0xfm0gKO4Vdony1XQREQAREQAREQAREQATyI5AWhZeTEDyYOZqZNzpcZyaS8ePH2wknnOC36T8REAEREAEREAEREAERKG4CaVF4mQK4a9euhi9vrDBD2h9//BG7WesiIAIiIAIiIAIiIAIiUCwE0hKl4b777vNT/A4fPtw6depkjz32mL399tt+XuQnn3yyWCqik4iACIiACIiACIiACIhAPAJpUXinT59uZ5xxhh1yyCG277772tKlS61nz56GAnzFFVfEO6+2iYAIiIAIiIAIiIAIiECxEEiLwlutWjULB6btvPPONmbMGF/4HXbYwRjQtmDBgmKpjE4iAiIgAiIgAiJQdgksXLjQBg0aZN9//33ZhaCaxyWQFoV3zz33tP79+9vUqVNt1113tS+//NLmzZtn48aNszVr1litWrXinlwbRUAEREAEREAEyhaBjUt/tzV33GqrLzrPVl95mW38Mz3jfEaPHu11EJTdww47zJ544omyBVa1TUogLYPW1q5da927d7fatWvb66+/bgcffLB9/PHHPlTZaaedZs8880zSQminCIiACIiACIhA9hMIXCSn1eeckbei5ctblXsftNwaNfJuL+DaLrvs4scQ7bPPPjZ37lxr166dN75VqlSpgDkpeTYSSIvCG4LBd7dmzZpGOLIhQ4ZY3bp1rUuXLuFu/YqACIiACIiACJQBAmsHv2EbZ83apKYbFy+ywFl4YyWnxjaWW69+7GbLbdTIKh3bd5PtsRvWr19vW221le9VzsnJ8bubNm1qQ4cOtVatWsUm13oZJFA+nXWeNGmSd2OgcTHFML69EhEQAREQAREQgbJFYOPcObZhyqSUKx38scw2uL/NFdwoq1evbqGySz4Y4BYvXiyFd3OhZtlxaVF4mWWtV69e/ktqyy23tAsvvNAPVLvqqqts4MCBPnpDlnFTdURABERABERABBIQqHTq6WZ/r91k7/oZ02zdgKc32V7x5H5WvlWbTbZbxYqbbouzpbxzi8DKGy0Mmq9atWr0Ji2XYQJpUXgffvhhGzt2rM2cOTPir3vKKafYzz//bLfccosU3jLcwFR1ERABERCBskcgd+v4/rgVnatj4GZl/eedIWbOQOa0VKtw0MFW8YADCwUJF8oVK1Z4l8owatSiRYuscePGhcpXB2cPgbQovKNGjbKLL77Y8JcJpVy5cnbdddfZAw884OPy0rUgEQEREAEREAERKNsEKvU6xip06mwbf/vNcl0Up9wG2xUaSIUKFfyAeSJGXXTRRX4cUZ06dfxg+kJnrgyygkBaFF5GQK5cuXITILNnz/YO5HQ1SERABERABERABEQAAgxQizdIrTB07r77bjv88MN9pAaMbi+99FJhstOxWUYgLZpojx497LLLLrPWrVvbunXrDAWYWLw33HCDdezY0TuSZxk3VUcEREAEREAERCCDCDRv3txmucgQv//+u+L/Z9B1yZSipC0sGS4Njz76qAVBYHQtoPgy6xohQfiViIAIiIAIiIAIiIAIiEBJEEibwkvhZ8yY4cOS/fnnn7bjjjta165dvfJbEhXTOUVABERABERABERABEQAAmlVeCdOnGiMioyVAw8s3OjL2Py0LgIiIAIiIAIiIAIiIAKpEkiLD++PP/5oPXv29GHI4p0YNweJCIiACIiACIiACIiACJQEgdx0nHTAgAHWsmVLb90l8DMTUUT/peMcykMEREAEREAEREAEREAENodAWiy8CxcutG7duhkx7yQiIAIiIAIiIAIiIAIikEkE0mLhZVa1F1980ZjLWiICIiACIiACIiACIiACmUQgLYPWcF84+OCD7aOPPvKxeLfaaqs8dSQmr0QEREAEREAEREAEREAESoJAWlwa7rjjDhs7dqz17dvXhyMriYronCIgAiIgAiIgAiIgAiIQj0BaLLyHHnqodejQwW688cZ459A2ERABERABERABERABESgxAmnx4WVK4b///rvEKqETi4AIiIAIiIAIiIAIiEAiAmlxaTjzzDOte/fu/hx77LGH1axZM8/59t9//zzrsSv//POPfffdd1a5cmXbZZddLCcnxydZtWqVTZo0KU/yjh07+vW1a9f6Wd1Iu+eee+aZ0Y15tMmvUaNGmtY4Dz2tiIAIiIAIiIAIiEDZI5AWl4azzjrLnn766YT0kk08wcxs559/vnXu3NnWrFljU6ZMseeee84qVapko0aNskcffdSaNm0ayfuuu+6yv/76y0477TRr1aqVLViwwKe9//77vaI8fvx4u+GGG+yggw7yg+j69etnRx11VOR4LYiACIiACIiACIiACJQtAmlReLHQEqkhkWyxxRaJdtkjjzxiVatW9QosiVBW27dvb4cffrj179/fK7OEPYuW559/3v7880+75JJL/Oazzz7bTj31VMP6S9p//etf1rZtW1u8eLGdccYZNnjwYKtYsWJ0FloWAREQAREQARHIQgLLly+3zz77zHr06JGFtVOVNpdAWlwaKlSokMeloCCFQVlldravv/7auzTQULHgIjNnzvRhzm666Sbv6oClFheGWbNmGW4SY8aM8eu77rqrtwy3a9fOfv31V6tfv7598MEH3qWhSpUqNn/+fGvcuHHcYm3cuNHWrVsXd1+2bKSOfJCUL18+4i6SLXWLrQc9A6FLTOy+klynlwM3nGwW6si9XK5cOcvNTcvwgIzFxQd0JtZR7Sxjm8xmFYx3K/dTPPn000/tl19+ibfLb6tXr57v6UyYoAR3LHZjfv71wySbsWq11apU0V5uv4fVSJNRip7i4447znOTwluCFzkDT50WC29h6hXt0vDTTz95n10ssvgBH3bYYd7NoU2bNjZx4kTbbrvt/AQXuDNg4d199929S8Mff/zhl0844QRv0eUBEbo08GK6+uqrDd/ieIJ1mryyWVB4+YNLJiqD6WRPu8lERYQPjmXLlqWzqhmXF8oW9SwL7WybbbZJqIiU5IXhGjCGIZslbGfc55l4r6eTffXq1bOud3KNe0Y0ef9D45N4o/tjxE5Fdy3HHbCfbesMFoURxvxgGKtVq5bXIYYNG1aY7HRslhFIi4W3MEzeeOMNr9jSQLHyoph+9dVX3qVh6623tltvvdV22203b7k9/vjj7eeff/YKavPmze3//u///Kl79erlt/GiXb16tT388MPepaFPnz527LHHestmojLywEzmcpHouNK0HasbUTSwfmb7CyJTFXrKle3tDGV3yPwFNuyP5e4t9p+Bp+m+TzrX2NrO2L5BurMtcH6Z2s6oSLa3MxRenvOF6Vks8AUvoQOSPa9nz55tGHtipWHDht7AsWLFCmvWrFns7mJbf3DmbPv+T/csiJGZK1d5JRdlFwnc3zpnkDn0i6+tRbW8k1axv7XbduXOO7GYr6xcudJeeOEFW7hwoR8LlO8BSlCmCJS4wotLw8svv2yvv/669+e9/fbbvUsD3RIMSMNdAcG6i0I7efJk44FHd00oNWrU8G4JfA1jydx55539rjp16vi0YbpEvzw4s1nghVDPZA/QbGBAXTNVGcn2dgb3X9eusy/ivOTS1bawAGU7x8Kwov1nO5/wecb7INvrmuxZ9uKLLxpuDbFy8cUXe/epb7/91u69997Y3cW2/v0ff9rI35akdD7eUPOcKyN/sfKX+5BOVfbaay+flF5iiQjEEihxhZfpiEePHm1PPPGEDRgwwFtyDznkEP/livLK1xrW3Hnz5nlltnbt2t5iO2HCBB/FAb9Ivubw26XLmAfgq6++6hVivn5Zx8KZSLBKxftKTpS+NG/HPzrbhY+cTBRe0mWhnfXetqZ1rVE96SXY6Fj0njLDW3n+8ylmxoPoyWY72jYVkj+StnRKTiZw3HbbbTP24zET+CRtAGnaiVGEv2yWZK4zJ554ojHpU6zssMMOxnOQ3s2SlFtbt7Cr/tnUMvuVe09fP3mat+xGl+/65jtbF/f8iJUt3dgTiQikg0CJt6Rnn33WR1MIb1y+aFF8cTpnkBUKL13xKLYor0R0wJKLa8Ntt93mlWAGpqEI88XPH2HN2MbDsFq1akm7+DgHrhPZLAzKC1lku4U3mUWkJK8x3LO9nfHxaK5LsYG7n7ivEskzc+dZRXefr3OKbygsvbdipd3fqkW4KaN/M/U+ov1nezvj4/FT5zrz0u/LUvro4APr741u0G5OrvcVTaVhNa1axa7YMf5A51SOT1eaZPfRSy+95CMRcC7GokyfPt37rT700EN24IEHpqsIm51PQ/cOjietqlezOav/sqd/mWPV3Qfuho2B9XFuSuc3LXne8cqrbdlDIPFbqZjqiDWWkGK4L6CYffjhh17BxSrLTUyYsd69e3vFFWd0LLn4oxJ27Oabb/aRHfDhRSFmwBL7HnjgAR+7l23HHHOMd2BPVB2syGFUiERpimN77jdjLWfJb6mdyj28na+GWa4bvetecPkJdfygSVObWLmqS5p/+vzyi7d/H2fV29f5V5a0ZHKUhkxoZzkTf7TcX35O7TKt/8c517k/N4rayuX/qEARKde4ia1t1drfu4lO8vuav+wf2m+U0Gm5+K+/M+JejCpWwsVMDnOYCe0sIbg07KCdLXbvio+WFt0g0CV/b2l/1a+bhtIWLgsUXow48eTGG2/MsxnLPjHtcfHLdLnFWX+PdUruL84otb2bcKrt1sl7hTK9Pipf6SCQ/1usiOsxfPhwPwDhzTfftCOOOMK7H6C0cKNjyZ02bZpXYLHYciPzx4xsuC+MGzfOK7ooyqFlj18iOixdutR3ffKwyK/bKxOsNbnjv7fcaVOKhDaPy29rbGuDl68ukvzJlC/1LjW3KbL8U804bAeppi/OdBnRzmZMs9wvPi+yam9AkW3dJqnlrZ17uT27YNEmSu9+29RIelyRFTrLMs6EduYevpbze2r+mwXFj8Lb2b0fXm/TwimDiV9hf21YbydOnLpJ9mdvV98OqrVp13l0wirlSl8ECN6N3bp1s08++cQI0Znp0tpZevmTiEBxEUj8tCimEgwcONCWLFnilV1Oif8uFlsc77HwErkhdGkIB6ph/cWnN9qlge7U3377zSvDzz//fMSlgYcAynHjxvG7S3h4cp6Sltwu+1uOC7OWVFy4oQoffmAulpxP5v93bh7/HHHUf6xwCQ7eSJdRnbq2b9UtIx8G8ZL+tWGjXTX758gubMFbuK74mxvvYFu4F0AyaeQmF8kEjlzPTFR6M6Wd5bRrb7mN4t8Lkevr2lnFEcMjq+HCusNcEHd3PyUSmuXX1arbl7/Mzfca7FR5C5viLL0I7ay2a8dLXI/MvT/9wqaE0tx9+HavmbgMCQ8sQzsy4T4sP+4bKz/snSKjXmuXtlbtxFOSfiCNWLrKtnTPrVXuuRYto5f9YRc0+N+g5+h90cuZwJHnRiJhVlHGr4RCVAbcAZl4qawLOgR/EhGIJlDiCi8uDcyuhkWWaYWJm4ffLYKLwh133OGV1VWrVlkYlozBVx06dLBrrrnGK7g45xNLF2suLg2EJcNxH5eGUhOWzFnF8pWHH4gou6RFUQhcnSs45cE6dkp4OB8IzRyXXZ2ykMz6c+30WZHYiGTGoxb/t/nu+Au2a5gw/0zakYnKLnwoV0aEi2rqBpHwl0wGDdxkb+DKX8HdT8naGR+d453COsBZbwsitLPF7qPzmRSOO7L2tnZkCspKQc6/OWkztZ1Rl4xoZw13sKDDXvmgdVd+7Nc+jX+WuSX/u7uLmV4h2cyYLt5zvfp+TEeyKA1buBjsuT7HvMXgGZgRjPIWK+5asuc1g70xCIUCC0JxHn300eEm/YqACEQRKHGFl5ckUwIzaI3Z00LBDSEMS8bLhYEYKLTRYcm22uo/Mfuw4uLWEB2WLHygJftCDs+V7KEZpsmE3/X47sYI1t5cxyfXPewSSciAeiZ7gP7pugDz2kLM1rr8V7m/0sKIumaqMlJaGG6oWGGTEdQwzXHtp1ySdkaaLs5doYnrpsx192pRyPauJ6G0cCyK+ueXJ+0/I/i0deEk+UsiG0d/YhudW5q55w6CsuseUJZb3rWz085gLa5QxzmuV3COG+SYrK4VGTjpM/1fNhVcG+2+bS37yh2bTKq7Y9tmQHd7smfZpZdemqwK2icCIhBDoMQVXtwV8N2NFRzwGWw1duxYrxBPnTrVx9QNw5L9+OOP3pqL5Tc2LNm7777rB6sxlzYPxNIQlix30ULLccHUk0k5Z2Wt5KZVziPO6rbaGUqCcd/m2Ry7krNtbVseuzFmfW9nAR5lSy02iNsebtBSaQl1pLBkMRc1ZrXczBmWu2B+zNa8q7nOBQbFIHSd8XvdvUjvSTDkrbyJY9aaOcvejm7gWpFJsDEj2qLCkhX+ClfEdcZ9xOfRSV07+8f5/q5wz/9kMs5NXnD1z3OTJYm7j8GSD/48J+6+6I1tXZSG55vn0xMSfUARLScLS8YpcbvA0ssMY8wu2qpVqyIqibIVgdJPoMQV3kQIGbSGlZYQY/379/d+vii7hCXDosso6b59+3qLJTd5GJaM7QyAe/vtt/0+tofW3njn4jyZEMZnw8DnzaZMilfEfLdVHvRCvmke7XWcjd56m3zTVXU8ljsXBoQXUW2n7PZfvMT/+Y0J/ju2fj07Tl3NCehguMqMsGQbZ82w4NPRCcuZbEelUR8n2+33re3S1Sq32SVpWLJ8MykFCZL1lJRk8bEIZsLzLBUGQYuWtpE29d/nTXhMhV12sUpJQkVi4d1u9Ro7pq4LRenuq/wEl5l/3EdcrnuglXd8UpEd3MDoTODI+ymRYPQhChERi/Dn/eCDD7wC/M477/j3ZKLjtF0EyiqBxHdTCRHB4ovgv4sPLv64WJbw6w3DkmFd6dKli7f8ErHhscce8+HKwrBkQ4cO9eGNeGD17NnT70tUnYwJS7bTTpbjrAopCYPsnCuI0/p9F2B+x/CCmLtFZZuQTzdebD7et9LNnLXY/eUne7npHzMhHBLtJ1k3YH71KKr9XINM4JPTpq3l1No2tWrSxly5HVAXlix/F4XAKRXr3QyIgbtvM2HAT2qV3LxUCkuWnFvu559aufffS57ov3tDFZTnDW0t+GCEbXR/yWT3Vm2s7XHHeze3ZOkKsy8T7lcU3kRhyR555BG77rrr7OSTT/bvwhEjRtgll1xib7zxhvXr168wVdexIpCVBDJO4Q0pc6MzMI2vVb5icU/AssvfPvvsY++//77/ZbDal19+aXfeeae3KiU6Jsw39pfz0G1U4nJ07yIrAh8Ml7ruw+tdPcsRu7cIpGr5coZ1WBKfABbBjGhn27SPX8A0bEXJ/d21sxru4zSTFcI0VDVjs+BjLxPa2TpniIieWCQhMJ4ZWGmdK4P/UOXDio+sfCTHubbQ20e4yrIquJntu+++eapPbyfRiiQiIAKbEnBueik8XTY9rki2oMgy1XBo5Z0zZ45deeWV/gsXhYFoDs2aNfM+uUw6gd8S25mVDaUYSXRMkRRYmYqACIiACIhACRCgd5LXNxZgPjYx3jBehd9M7OUqAUQ6pQjkIZBRCm+ekkWtYMWN50+10k1jiksDN3isJDomNp3WRUAEREAEREAEREAEsptAqVB4s/sSqHYiIAIiIAIiIAIiIAJFSSD/Ia5FeXblLQIiIAIiIAIiIAIiIAJFTEAKbxEDVvYiIAIiIAIiIAIiIAIlS0AKb8ny19lFQAREQAREQAREQASKmIAU3iIGnO7sCTY+e/bsdGcbNz/iICOEv/n000/jptHG7CSgdpad1zXTalUS7QwGH374oa3OZ2bLTGOl8oiACBSOgBTewvEr9qMJ2/btt8mnEU5Hob777ju79957fVYovMRBlpQdAmpnZedal2RNi6udEa6LCRpC4bxSeEMa+hWBskFACm8xXWfiJc6fP98WL14cOeOqVatszZo1kXWW+ePhzL5169bZ3LlzXUz2jZE0sQvEXyT2MMeEQh4cu2DBgsj2eOcP08f+cr5ffvnFvxB4KTRs2NDP4EO6MG+2U59QOFdoEQ63sf7TTz/pxRICKYbfeNeZ67B8+fLI2WkzrBeknXHwvHnz8lzLsC2onUXQlqkFQj/+/PPPkWdMonYGFNobbfPXX3/N88yLByy2ndFOed6QB+cMJfb84fZ4v6RduHCh761i/7XXXutn8wzbcCrPs3j3VrxzaZsIiEBmEtg0gG1mlrNUl4p4wZdddllEwdhxxx3t9ttvt2+++cYef/xxGzhwoH8JnH766XbHHXd4Bfeuu+7y6evXr+9dGB588EFr1KhRHg5Mqfz555/7GMVLliyxe+65x5o0aWL9+/f3SvCMGTPsoIMOstNOOy3u+RMFJ+flwAx3vGBee+01P+Md1t7nnnvOnnnmGa/oLl261L98mNlng5uCdtmyZf6FwoQgu+yyi2EhZpnyTJ8+3S644AI77LDD8pRfK+klkKid8VI/5ZRT7MYbb7Q99tjDrr/+emvcuLF17tzZUmlnX3zxhT311FO+ndGmzjnnHD/Nt9pZeq9facrtySeftOHDh9t2221nPHvuu+8+22qrreK2s7PPPttPdcvzi49pPtBPOukk69WrV54qJ2pnU6ZMsYceesg/b4i5/uqrr/r2GHt+PswTCc9YFNbbbrvNz8p56qmn+mnr33rrrZSeZ4nurUTP0ETl0HYREIESJOAeApIiJjBkyJDg7rvv9mdx1org8ssvD5zvml+/5ZZbgvvvvz9wc6AHL730kt82ceLEYO+99w6c9cSvv/3228F5553nl92LJXjllVcC51MbuBdG4OZ799udYhq46ZX98gMPPBBceOGFgXu5BE4ZDZKd3x8Q5z83dXPgFCS/x81oF7i52f2ye/EETnn1eTsl15dz6NChfp97Efm6sHLxxRcHzvXCb3fKcdC3b19/jN+g/4qEQLLr7JSJoE+fPgHXyCkggbPyBqm0M6csB927dw++//57X+ZFixYFhx9+uD9e7axILmPGZ8p9f/TRRwfOouvL+u677wY8F5B47YztPXv2DJ599lkWA47ff//9AzcFbhA+z5K1sx9++CHo0qWLT8/zLNn5/Qni/OemVw/cNLyRPUcddVTgLMm+3Kk8z5LdW5FMtSACIpDRBGThLYaPjVGjRvmzYF1A8IkdPXq0tWnTxi699FI78cQTvaXEKYV+P/9tv/32EYtup06d7JFHHvGW1DDB5MmTje1bbLGF39S1a1fvo4YlGWnbtq2fXhILRLLz+8QF/A8LLvnWqFHDz2XfoUMHn0OtWrXMvZx8/RiMMmLECPvggw/8PiwklLl169YFPJuSp0og2XXGmosfNlZZ92EVmZ0wv3aGNa5ixYq26667+mLUqVPHt81x48b5dbWzVK9O9qSjVwlLKz1KiFMmDSus+8j2vQbx2hnpnMLJj39u7LTTTjZt2jS/zn/J2lmVKlWMnq5tt93Wp092/s2xuOb3POOkye4tXyj9JwIikPEEpPAW0yVq3769V0LD09H9h+BX5j6JvH8kbgIojbHirLhe6WDO9FB4+EdHa8Bnl7/c3P+4ZTPlcrQkOn90mlSXY/NGIYoVXog9evSIKFbOwmMNGjSITab1NBNIdJ3xg5w1a5ZVq1bN+BipW7fuJmeO185oj3RD8xe2P3w1cWNBYttCovNvcrIUNsTmrXaWArRiSMLzClcG7ulYSaWdcQxtLfxYZ70g7SzZ+cmroJJKOyPPdLbtgpZR6UVABApPQIPWCs8w3xywvmIB2XnnnQ2f15dfftmmTp3qB3nh5+q6/70/G/67PMwRBm6goCCMKN5tt938cvif6+KzsWPHRgZhYE1t0aJFROEN0/Gb6PzRaWKXUS5QbDZHsPxST9f97X+xCuKzHCpJm5OnjsmfQLKj5oucAABAAElEQVTrPGDAAP/B4dxnfG9BOHgyv3aGIoJv5JgxY3wBGITIH205VpKdPzZtuK52FpIoPb/77bef98uvV6+ev79pQ87Nyvf6JGpn1O6TTz7xlaT98Gxo3rx5pNIFaWfJzh/JMGaBD3CEAZubI5vTtjfnPDpGBESg6AjIwlt0bCM587Akjm3v3r39wB8GDHXr1s0YdMay82fzA9RIM3jwYP8i4AVw0003eQV4yy239AMtIhm6BfYzCOy4447zo41JE7pMRKdjOdH5Y9NFrzdt2tSHJbv66qv9AJPofaksM1CO8qPcYx089thjfTlTOVZpNo9AouuMm8l7771nL7zwgu9Oph3eeuutduaZZ/p2lKydURLS8WHGgEVcU0hP+4uVROePTRe9rnYWTaN0LG+99db+fsYFC5cYLP88JxK1MwacIfQsnHDCCd66S3sKe7nCWidqZ0QBiZZE549OE7tMGXFdOPLII23QoEGxu/Nd35y2nW+mSiACIlCsBHKcRfE/JsViPW3ZPBmhb7A0VKpUKSkAN0jM3IAgw1qCghH7Yog+GIsFPnTJ0oTpUz1/mB5FlfzzK2+YPt4v/spYfCXFRyDV61yQdkbpU72WqZ4/JKJ2FpIoXb+4L+CakMqzxw0Ss0cffdS71PBxnszXNtV2VpDzh2R5Vka7UoTbU/0taNtONV+lEwERKHoC5Yv+FDpDSKBq1arhYsq/+b1MKlSoYPylItHnX7FihX388cdxD8M/b8899/TuEYVRdslcym5cxEW6Mfo6p3qi/NoZ+aR6LWPPz6xWxJWOFZQe/EDxO1c7i6WT+et8vKfSbqJrkkr6VNtZ7Pnza2eUozDKLsfHtm22SURABEoHASm8GXid6CY899xzi7RkKBl0DcYTRkVLsp9AcbQzKDJQLvShzH6qqmE8AldccYVts8028XalbZvaWdpQKiMRyEoCcmnIysuqSomACIiACIiACIiACIQEFKUhJKFfERABERABERABERCBrCQghTcrL6sqJQIiIAIiIAIiIAIiEBKQwhuS0K8IiECpJsBATIkIiIAIiIAIxCMghTceFW0TAREoVQQuuOACe/jhh0tVmVVYERABERCB4iMghbf4WOtMIiACRUSAWQclIiACIiACIpCIQDk3a9JNiXZquwiIQHoJMJHHOeec42fYu+WWW+yJJ56w3377zVq3bm1Ms4tgqST4/uOPP24DBw70+4hN2r9/fz+bHrPXLVy40Nq1a+dnufr666+NaakPPvhgH9M2LPEll1ziQ4IRfozufs5399132wcffGDVq1e3Jk2a+KQczyxshKO7/vrr7amnnvJTv3bo0CGSH9NMMy3xgw8+6CdE+fbbb61ly5Y+tF1YJ+pw11132T333OOnvWY67Oi4qxxz3333+RkGf//9d2vUqJExCQGSrHw+QZL/yHPYsGHGdMlLly71094ya+Fee+0VOWru3LlGaCympX366adtyZIl/hhmnGMGMGJPb7vttpH0EyZM8CyYLOG7777zs3RF1yWSUAsiIAIiIAKlgoAsvKXiMqmQ2UJgw4YNforeAw880M9SdcQRR/gZqE455RQ/jTT1HDFihJ111lk2btw4P2EDSiFTNTN960477eQV3TvvvNMOOeQQf8yOO+7olbjoiUTGjBnjFUv2MTvUHnvsYe+//76fWpWYuExL/eKLL3qsM2fO9IrsSSed5KeK7dixo1177bV5pqru3r27vfrqq3bAAQf4837yySd+ympmSQvrdOihh3qlkzpRloMOOihy2bDAcuxPP/1kzLr11ltvWa9evfz+/MoXySTBQvPmzf2EAA0aNPBKOLwof7RPLwo9SiyxWlH4+/XrZ6+99pqfmpsZ55jee/78+f4M1A1lmckyjjnmGK+8My1t7BS3CYqjzSIgAiIgAplIgKmFJSIgAsVDwE3FylTegVNwIyd0FkS/bdSoUX6bU2SDZs2aBW7qVL/+zTff+P1Dhw6NHBNue/vtt/22o48+OnAKa2T/2WefHfTo0cOv33777YGbISr4888/I/vZVqdOncAprIGzIvv8naUzst8p3MHee+/t1501NnCKXzBlypTI/uHDh/tjFi1aFIR1ctbhyH4365Xf75REv428zj///Mh+Z2ENnJIZTJ06NcivfJGDkiw4a3fgLNg+BeVxk6oEzz77bOQI96EQPPbYY3798MMPD5ziGzjLdGS/+zAIzjvvPL/uLNOBU8oj+1hgW3T58+zUigiIgAiIQMYT0ExrmfgVojJlPQGss6HQ9U93+vfff29dunTxm7HIlitXzi+PHz/eT72LhTQU3Bnq1q1ruAkwPe+pp55qffv2tTVr1vjjXn/9de96QHqnHPu0uD2EgjUTF4Bff/3Vb6pcubK1adMm3G0NGzb0x7GhZs2aRn5YSJ9//nnvMvDZZ5/5tE659O4RrLRv395v4z+OR7DeuqegP/byyy/32/ivVq1ahiUVya98uGQURJg+FhaDBg3yXL766ivDpeG4446LZNOtW7c8s79hjcZ1AdeNH374werVq+ct6uEBXAss7hIREAEREIHSSUAKb+m8bip1KSewww47RGqQk5PjfWHpQg8FJTMUZ5n1+52VNtxkHFO7dm3vTsBG/HfpyndWYO8LjILmLJk+Pf7A+OcynXQoKJHXXHNNZFvsdNKkRVFF/v77b69Uf/nll9apUyfr3LmznXDCCcZ6tESXLzwXeaD0Urfo/dHHpVK+6PSpLPMBgGsGij2uG87anWdqW/yHo4VpbyknbhC4acAyrAPpcEHBj1oiAiIgAiJQOglI4S2d102lLuUEsG6ikCFYH/Gj3X333ePWqmnTpt4ai4V111139WkYtMZgK3xVEfxyTz75ZO+XyvKJJ55oFSpU8Ps4fuTIkcYArVCJmz17tuHnGz1QyyeO89+QIUO8Ty7+t6G1lW0IymF+gvKIcj59+nTDsopwXJ8+fezCCy+0wpYv3vn33HNPa9Gihb355pveX3jAgAF5koXW5XCjc8Hw/OGBn2/9+vXNuVqEuz2/kGdkoxZEQAREQARKDYH/mXxKTZFVUBEo/QSIvkBXPm4FREZA6dt3333jVgz3ByzCN9xwg1eMcUO46qqrvBIZfQxWTRRb519r/dygrFCcP693Xbj55pt99AeUZSy0RDYII0OEaeP94jrBwDTKisyZM8euu+46v4z1NxU588wz7d577/XlW7dunT3yyCP2xRdfGIppYcvH+bGIO39gH70iLA88GNyHNZxBd9GC28Jzzz3nBw7yy8cEAweRc88911uF33nnHV9v3DeOPPJII7KERAREQAREoJQSyHgvYxVQBLKIQDjAy1lgA+c3G1SqVClw/rqBs35GasmgNTeRQmSdBQaUOQtw4Cy0kWMmTpyYJw0rzmrsB1jF7nChzAJnZfXHMmCLQW4MOEMYtOYUxjyHMJCsbdu2kW2nn366H+jllN/AWXkDF7HBDwxzfrKRQWvOahpJ7yzWftDajBkz/DbnWxwwEM65WgTOxzZwrhEBA99CSVa+ME2yXxfeLXDKe+DCi0WSUT9n7Q6c73BkGwsMWmPAHAPVOMZZcwNnAY6koawMUHMWXX+NnPtDED0gL5JQCyIgAiIgAqWGQA4lLaW6uootAqWOABZRBojRpY4v7PLly1NyKwgrumzZMm+x3Fx/Unxa6bZPxbIbnjP8xTKLlZPu/s0VBoXhk+wiRMTNorDlc5EtvL8ymRPfGBcMrLmELgsFf162E+eY81EfrMCxQnxhrNrE6JWIgAiIgAiUbgLy4S3d10+lL8UEUDpT8aGNriKDqwojxKrdXKG8hVF2Oa+zaCdUdtkfWz58ffPzE8ZnGaF8/DHwbNq0ad5tApePaGXXJ4z6L/Z8Ubu8D7SU3WgiWhYBERCB0ktACm/pvXYqeSkkgCVxcy2spbC6hS7yTTfdZE8++WTCfIhGgU9ytGCFZkBgOBgueh/LLkZvnhngYvdrXQREQAREIPsIyKUh+66paiQCZZ4AriPE45WIgAiIgAiIAASk8KodiIAIiIAIiIAIiIAIZDUBhSXL6suryomACIiACIiACIiACEjhVRsQAREQAREQAREQARHIagJSeLP68qpyIiACIiACIiACIiACUnjVBkRABERABERABERABLKagBTerL68qpwIiIAIiIAIiIAIiIAUXrUBERABERABERABERCBrCYghTerL68qJwIiIAIiIAIiIAIiIIVXbUAEREAEREAEREAERCCrCUjhzerLq8qJgAiIgAiIgAiIgAhI4VUbEAEREAEREAEREAERyGoCUniz+vKqciIgAiIgAiIgAiIgAlJ41QZEQAREQAREQAREQASymoAU3qy+vKqcCIiACIiACIiACIiAFF61AREQAREQAREQAREQgawmIIU3qy+vKicCIiACIiACIiACIiCFV21ABERABERABERABEQgqwlI4c3qy6vKiYAIiIAIiIAIiIAISOFVGxABERABERABERABEchqAlJ4s/ryqnIiIAIiIAIiIAIiIAJSeNUGipzAm2++aatWrSry8+gEIiACqRMYO3asTZkyxR8wePBgW7lyZeoHK6UIiIAIlDICUnhL2QUrjcW96aabbOnSpaWx6CqzCGQtAZTc0aNH+/q99dZbUniz9kqrYiIgAhCQwqt2UCwEgiCw2bNn27Jly/Kcb8OGDfbTTz/ZrFmzbP369ZF9pPvnn3/sl19+iWzTggiIQNEQeOSRR6xOnTr+HlyxYoVxX86YMWMTJZj7+Oeff7b58+cXTUGUqwiIgAgUEYHyRZSvshWBPATOPvtsK1eunE2bNs0uvfRSu/DCC/3yaaedZg0aNLDFixfb33//bR988IHVqFHD9tlnH2vZsqVNmDDBXnzxRevYsWOe/LQiAiKQPgJdunSxIUOG+J6Yq666yiu8W2+9tU2ePNkGDBhg++23n/3555/Wp08frxQvX77c358DBw60nJyc9BVEOYmACIhAERGQhbeIwCrbvAS6detmw4cPtzFjxtgdd9xhv/76q18+77zz7I033rDPPvvMqlWr5n/DIw855BBvZZKyGxLRrwgUPYGJEyfaU089ZUOHDrWLL77Yf3ByVtbbtGljn3zyiX3zzTde8f3222+LvkA6gwiIgAikgYAsvGmAqCzyJ3DEEUf4RPXq1fOWoR9++MFOPfVU++ijj7wCPGnSJO/asHbt2khme+21l6xHERpaEIHiIbD99ttb8+bN/cmaNGliX3zxhV9G4UUuuOAC/7tkyRJ79913rX379n5d/4mACIhAJhOQwpvJVyeLypab+7/OBFwXeJFee+21NnXqVDv++OPtuOOOs2uuucbwEQylatWq4aJ+RUAEiokALkWhxLordO3aNY97EW4PEhEQAREoDQQyUuEdOXKkvffee9a5c2fvMxYLEmsgIXXw7yxfvry3GO62227Wrl077ws6aNAg7x/KIIymTZtav379YrPQejETeP31171Ci08gg9FatGjhr+HVV19t3bt3twULFtj48eOtR48exVwynU4ERCAVAj179rRRo0YZ/vgVKlTwz9XDDjvMP2NTOV5pREAERKAkCWScwnvKKafY3LlzvX/Y999/bw0bNsxjUQAWXWsjRowwrIZYBMeNG+cjAKDw3nrrrV4RDgdIffXVV1J4S7KF/ffcM2fO9NeR6AsPPvig30rX6I033uj9BbEk7b333n4EeAYUV0UQARGIIYDCO2zYMMO4ULNmTe/2cPTRR8ek0qoIiIAIZCaBHKcw/q8PuYTLyIAmLH4oPyisPFSxJLzyyit5SobFlkFP+H9iMTz33HO9ZfCKK67wo/vxQXv55ZcNSzCDohgsteWWW+bJQyvFT4BR3lwHrPKhbNy40RjxHd2NGu7TrwiIQOYRYIIKnstbbLFF5hVOJRIBERCBBAT+51iZIEFxbv7uu++scuXK9uyzz9o222xj9evX9y4KsWXAsosPKNYG/ECrV69uixYtsnDAE/tQfnF5QHmmq1xS8gTw94tWdikR11LKbslfG5VABFIlsNVWW0nZTRWW0omACGQMgYxSeHFlYBQ/frcIyi/+nrHC6GDituLKgJKL5ZABTgyAQogAgPUBX14siFiDJSIgAiIgAiIgAiIgAmWTwP/6ljOg/lj/UFBDYbaf6NH94fa6dev6kFb4gtK1hp8vU9eyjLz99tvekrhq1Sojluvvv/8eHqpfERABERABERABERCBMkYgoyy8WHfxDwsFhRUrb6xUqVLFXnvtNW/lfeKJJ4wuNlyRUYSRv/76y/+GfruxoXX8Tv0nAiIgAiIgAiIgAiJQJghklMLbu3dvYxQ/s/igwDJnO+GrEEKVMUgNQSnGP3eXXXbx6aZPn26HHnpoxBeUQWzIlVde6X/PPPNM/6v/REAEREAEREAEREAEyh6BjHJpaNCggY/Jetlll/krUbFiRT8LFytMR4ullilq8dnF9QGll0kLkFmzZnn3B+JCEsN3n3328dv5rVSpkl/WfyIgAiIgAiIgAiIgAmWPQEaFJQvxr1mzxltxmTgimfz2228+zBUuDrHCxBR77rlnXB/g2LRaFwEREAEREAEREAERyF4CGanwZi9u1UwEREAEREAEREAERKC4CWSUS0NxV17nEwEREAEREAERyGwCRF7CVZFe39tuu63QhV2xYoVVq1at0Plcf/31fqptBtwzy+vJJ59sjRs3TilfolARWnX06NE2ZcoUH22KmWbDsKwpZZIk0cSJE+3999+PjGWKTUpo1yeffNJwIWWA/8MPP2x//PFHJBnzG2y33XZ2xBFHGO6lBZF169Z5t9NUJqfBPfWiiy6ye++9t8jje2fUoLWCAFVaERABERABERCB7CbAhFS9evUylFQmoyqsMKU9yl1h5euvv7Yff/zRK4Uoryi8DLRPRZhf4KijjvJjjUaMGOGjUTGjbPPmzW3UqFGpZJFvGhRelMhEwmRdN910kxENCyHM63PPPWefffaZffrpp/b0008bCviOO+5oX331VaJsNtmO0kxAAeZVSEUIPduoUaO0XJP8zicLb36EtF8EREAEREAERKBECEyaNMmHHn311VfTMiaH8T1HHnlkoety+eWX2wMPPODzwQIab5KsRCc58cQTfTSqX375JY8Sj4X49NNPN5RVJtMqjDCgPxzUn2o+J5xwQh4LOnMYEAGL8jKhVxjqNVl+BBUgclZB5Pzzz7dWrVr5utesWbMghxYorSy8BcKlxCIgAiIgAiIgAskI/PTTT3bJJZfYwQcfbEcffbTdc889Rjd3KOPHj/eTRx1wwAFG2FBCkcaTt956ywYMGGBYUM8+++yIpfHdd9+10047zTgeJZGu+2hBUbvzzjutR48edvXVVxtWYuS+++4zlMyhQ4dGIkCRN137WFzpvr///vvzKK9Yg7HC/utf/zIUQpQ5yoVLAAPjkfXr1/t6TJs2za+j9F133XV20EEH2bHHHmvPPPOMD6HKTs7/+uuvewtqrMUaBfrAAw/0aUibrGxYmFEUSRMtcP/yyy8NxZ7lUOD/6KOPemWf0K2pzEBbq1Yte+edd4zrSR1CSXR9V69ebddcc41P9n//93/24Ycf+mVmxL377rvtmGOO8ZG4cGGYM2dOmJ23cHfp0sVuueWWyLaiWJDCWxRUlacIiIAIiIAIlEECdOvTpU1M/ZNOOsl22mknr8igACEoiig3+Hei7BJutHPnzhYqi9HItt9+e9/djQW1ffv2hgL22GOPectlkyZNfJc7UZ2wQn777bf+UJQuZlh98cUXvcKNskX+lAuXASynhEBt2bKlT4/ijFJMOdu1a+cVZY5nLgAEZfess87y/rZ0/2PlRGGNthLjh4pCOH/+fH8MFlFcE7CwohRfccUVPl92oqiWK1fOK7Y+cdR/WDefeuopb+1kc7Ky4WqA28HHH38cyWHMmDGeD/sI1QqDUFCOcbvo1KmT//hAeU9FmNALlwOszkiy68tsuW3btvXp2rRpE5kMrHv37oaFng8U2H7yySfWtWvXPDPr8rEB1yIVd1ElIiACIiACIiACIlBoAs7aGjhFLXCWx0hep556auBi4vt1N4FU4BSjwCm+ft0pi4GzqgZu4FYkffSCU+oCN3gqsumGG24InFIYWXeuBMHWW28dOEus3/bQQw8FTlEOnKIbSXPeeecF/fv39+tOqQ2cJdEvO8syWm3gLL6RtOE2N1DOb3MKWtCsWbPAWXEjaVq3bh24WV4j65yLfKgbUqNGjcBZjSP7nUU6GDJkiF93ym/gFMjIvkQLYTmSlc1ZzwP3URHJwlnBA2fV9uuDBg0KttlmG7/s3BEC92ERTJ48OZLWWbt9mRcuXOi3ucF2wbXXXhvZH73Aefbee2+/Kb/r66y/Pl9nCffpnbU9cJbdPNd3+PDhPo3zI46chrLB0H0oRbale0E+vEX6OaHMRUAEREAERKDsEMCNAQsuVrypU6f6CATMkoqlEOnQoYMfCIVFlS5/LH64JaTqu3nzzTebU9K8WwFWYXxL//rrL/v77799/rhLMOFUdGQBrMLxhLRMTIXlMRSsvJQVi3HPnj395j322MNbZVnBfWHGjBmG9TmRMNjLKdnewor1GWswPqoIbgzMIYBVmAFbiSSVsrkPCevbt69h5cZqjIUUF5BYYZIuIlyEVm32c53CSb5i08euM2CQqA1Iftc39liuK+WiDM8//7x3CWFgHMJ1CyXk6RRfc8p1uDmtv4lpp/U0ykwEREAEREAERCDbCRC5gNBcDL7CjxT3hv322y9SbVwC8Nll9lQUNRRD3BNSjU5ANAHy53gUx969e1vt2rUj+eMykeqAL3xtnXU4T3pcLMgv2jc2WhlHsWaAWrKQW/ji4ie88847++gDziLs3SYo5O677+7rjS9vrDiLpg8jxsdCKmVD+YQn5xo2bJhXeg8//PDYbCOz05J/KLgfpCIcg98ybhJIftc3Nk94Uc59993XuzUwUVg8dwo+UGC/cuXK2CzSti6FN20olZEIiIAIiIAIlG0ChLrCV5aBTa5b3RgghTUzVCCxjjLoDEWX2LoorS1atPADqvIjh0Xwqquu8oPgsMCi/DIoLrSYcjzKc2yUAAai4b8aK8S8RUHG+hgK1mOUut122y3clOcXBZPjYs8RJkKJJ7wXlmysrQsWLDCYUAaUPxTebbfdNu4ArZdfftnXbcmSJf4c+ZUNpRXr+GuvvWaENcN3uEKFCmFRIr+cE0bR9Yz2/Y0kjLPANZw3b57Pm93UJdn1RWlFQuXauXJ4P2Mst/hDE7s4HKxHuwhl5syZ/phE3MN0hfmVwlsYejpWBERABERABEQgQgB3AAasoZyi9GB9fPPNN43BY6H069fPb0MJpruc2K2hBRE3hbvuustbJcP04S8KHtZWYsiiLKFcMuKfvEOXBizLX3zxhQ8ZxgA2lDxG/+OqgHA8rhYotrhT7LDDDub8gg2Fi8gFKNRYeLFIJhIUyEQKb+XKle3xxx/3Fl2stHBAgWWgHFZhFOY33njDfwxceOGF3nWCmLUoxIQ6wx0DJT7VsuHWMHLkSHN+sQbXeELdcan497//7euIQu98mjdJimL7+eef+1i8gwcP9hEXzjnnHD84MIxIkd/1dX7DPl8iYyxfvty7h3CdUd4RojMQwQIJrxnL8EQRDl1f2JZ2cQ1SIgIiIAIiIAIiIAKFJuAURz9AzXVd+0FTzj82YCCZU/YCpwD5/J1lNnDd/YFzPfAD2NzEEpF9zt/TD16aPXu2Txs7aM1FHgicFTdwimuw1VZbBVdeeWVw3HHHBc5PNlL2cMCW88/1A9gY6BYKg81c93lkIJxT/gKnwAbOnzYgvfPXDVxEgjB5wKA1N1lFZJ0FF2Ir2H///SPbYgetuUgMgXPjCGDAuairUwAj6VlwSm/gohcETgH29XWW2cBFrcgzaCu/soUZduzYMXCW0XDV/4YMwo0uukLgIl34OlJPBqg5hTKIHrTGevjnfHZ9ns49I8+AvVSur3Nh8Pm4UG7+9O4jJHAz2wVOmfXXw0Vs8AMNKWMoN954Y2TAXbgt3b85ZJh2LVoZioAIiIAIiIAIlFkCS5cu9QPHnFKakAGWWnxok/nDJjoYayzWwES+qKg2hAnDshp2s4d5EZOWwWf4k4aCVZp0LsJCuCnhL9ZhuvXxRcZPN5EQxgwLLy4MiYRyYFllGt947ggcV5CyJTpPuB1rM9dkc5iHefCb3/XFFxe+DKZDYE585NCdwW/873/4RGPhJx4yg/yKSqTwFhVZ5SsCIiACIiACIpCVBG6//XZzVui4URGyssJFWKkXXnjBD2iLnUAk3aeUwptuospPBERABERABEQgqwngf8okC0RUwIos2TwCWOIZqMaAveiwaZuXW/Kj0qrwYubHSTzWS4J4exIREAEREAEREAERyBYCdNsTTotYvpLNI8DgQwb3hYPdNi+X1I5Ki8KLoksA6UmTJsU9a6wCHDeRNoqACIiACIiACIiACIhAERAon448b7vtNu8A/uGHH1rDhg2Tzh6SjvMpDxEQAREQAREQAREQARFIlUBaFF7i17mwHdatW7dUz6t0IiACIiACIiACIiACIlAsBNIy8QQOx8x6IhEBERABERABERABERCBTCOQFh9ephAkdlqfPn3MBTbOMy81FXYBmjOt3iqPCIiACIiACIiACIhAGSGQFoX3rLPOMjcbSkJkGrSWEI12iIAIiIAIiIAIiIAIFDGBtCi8zJLBXMmJpLAzeiTKV9tFQAREQAREQAREQAREID8CaVF4OQmx1Jiyjmn0wnUCM48fP95OOOEEv03/iYAIiIAIiIAIiIAIiEBxE0iLwjtnzhzr2rWr4csbK8yT/ccff8Ru1roIiIAIiIAIiIAIiIAIFAuBtERpuO+++6xy5co2fPhw69Spkz322GP29ttv+2ninnzyyWKpiE4iAiIgAiIgAiIgAiIgAvEIpEXhnT59up1xxhl2yCGH2L777mtLly61nj17egX4iiuuiHdebRMBERABERABERABERCBYiGQFoW3WrVqFg5M23nnnW3MmDG+8DvssIMxoG3BggXFUhmdRAREQAREQAREoOwSWLhwoQ0aNMi+//77sgtBNY9LIC0zre25557Wv39/22+//WzXXXe1iy66yObNm2eLFy+2NWvWWK1ateKePNHGkSNH2nvvvWedO3f2sX1j002aNMnGjh1rEyZMsPLly3vXCSa/aNeunT8njX3atGlWp04da9q0qfXr1y82C62LgAiIgAiIgAiUEIF/PhttG50xLLd2HavQ9YC0lGL06NF27LHH+oHy9C7fcMMNdu6556Ylb2VS+gmkZdDa2rVrrXv37la7dm17/fXX7eCDD7aPP/7Yhyo77bTT7JlnnkmZ1CmnnGJz58619evX+2Puuece69ixY57jb7vtNhsxYoTl5uYaMX75Qzm+88477cILL/SKcLly5fz5K1as6MuSJwOtiIAIiIAIiIAIlAiB1VdfbsHSZWbr1pp7kVtOtepW5YGHLcctF0Z22WUXP4Zon3328XoERjCMb5UqVSpMtjo2SwikxcJLY+LLCt9dZMiQIf6vbt261qVLF78tlf9whSDSQ05Ojrfc1qxZ0x566KFNFN6ZM2f6BvzRRx/Z5MmT/RccaRGsvttvv729/PLLhiX4vPPOs1WrVtmWW26ZShGURgREQAREQAREoJAE1k+aaMHvv2+Sy4bp0yxwbgcRcSFNg5Ur7O8nHrXyrdpENocLOTVqWPm2u4arCX8xkqEb7L333j5Nw4YNDXfLWbNmWatWrRIepx1lh0BaFN4QFwrmuHHjfONiimEaW0Hku+++89EeHn/8cbvqqqusfv36XmmNzQPLbpMmTWzYsGF+hrfq1avbokWLDEszwj66M9q2beuVZ2IB88UnEQEREAEREAERKHoC/3z4gW2YMD61E7mJqzZ8M9b/xR5QrmXrlBReLLnoAhjMQsEQhmulFN6QSNn+TYvCyyxrvXr1sqFDh3pLKm4FDFRDaR04cKCP3pAKZlwZ6tWr5/1uSU+oMwa9xQoTXKD0olyj5K5evdqqVq1qU6dO9Ul/+OEH70uMLy8TYvz666+xWWhdBERABERABESgiAiU77S3lWuy4ya5b5g5wzZMmWzO5/B/+5ySmtu8hZVv0fJ/2/67lONcJVMRxvOErpBhevQHdAOJCEAgLQrvww8/7AeR0Z0Q+uvii/vzzz/bLbfckrLCS4NFQQ0FRRrFNlZwlTj11FPtwQcftAoVKhhdF7hTsIwQA5i8cGUgVNrvcbpVYvPUugiIgAiIgAiIQHoIVOiQd+xNmGvg3uurzz7D3OAbpmg19+I2Z9myyldeUygfXvSCFSuca4Sb4TWMGkXPb+PGjcNT67eME9hUm9wMIKNGjbKLL744YpklCwaNXXfddfbjjz9GfHvzyxrr7sqVKyPJUFix8sZKlSpV7LXXXvNdFU888YRttdVWfuAaDR4JpzcO/Xajuzhi89K6CIiACIiACIhA8RDIcbpB1aeftQpH9bJy7TtYhUMOs6rPPF8oZZeSY/BiwDwRoxDGEhGpicH0EhGAQFosvAxai1ZUQ7SzZ8/2YcmwtqYivXv3tsGDB9s333zjFVgsxC1b/qeLg1BlWHu7devmz0XejMgkQgMTX1x22WVWwzm3I4QhwZ3hyiuv9Otnnnmm/9V/IiACIiACIiACJUsAI1SlI3qmvRB33323HX744T5SA0a3l156Ke3nUIall0Bqmmg+9evRo4dXOFu3bm3r1q3zERS+/PJLHwOPkGI4kqciDRo08OHNUF4RQordcccdfplfbhIU3j///NO7PhCR4fjjj/f7GYmJQnzYYYf5GL7hIDV+FZLEI9J/IiACIiACIpC1BJo3b+6jMuDGWND4/1kLRRWLEEhLHF5yw6Xh0Ucf9RZXuhZQfJl1jYFs/BZEmKwCizHdEcnkt99+84PkcHGIFSamYEKMeD7AsWm1LgIiIAIiIAIiIAIikL0E0qbwgmjGjBk+cgIW2B133NG6du0aGUiWvQhVMxEQAREQAREQAREQgUwmkFaFd+LEiT4ebmyFDzzwwNhNWhcBERABERABERABERCBYiGQFh9eIjH07NnThyGLV2oGlklEQAREQAREQAREQAREoCQIpCUs2YABA3w0BWLeEfiZ+LnRfyVRMZ1TBERABERABERABERABCCQFgvvQjcvNtET8htkJuQiIAIiIAIiIAIiIAIiUNwE0mLhZVa1F1980ZjLWiICIiACIiACIiACIiACmUQgLYPWcF9ghpOPPvrIiMXLzGfRQkxeiQiIgAiIgAiIgAiIgAiUBIG0uDQwKQRxb/v27evDkZVERXROERABERABERABERABEYhHIC0W3kMPPdQ6dOhgN954Y7xzaJsIiIAIiIAIiIAIiIAIlBiBtPjw4sbw999/l1gldGIREAEREAEREAEREAERSEQgLS4NZ555pnXv3t2fY4899rCaNWvmOd/++++fZ10rIiACIiACIiACIiACIlBcBNLi0nDWWWfZ008/nbDMmngiIRrtEAEREAEREAEREAERKGICaVF4//nnHz/RRKKybrHFFol2absIiIAIiIAIiIAIpI3A8uXL7bPPPrMePXqkLU9lVPoJpEXhLf0YVAMREAEREAEREIHiIvD2/AX2y5o1tl3lynbMdg3Sdto1Ls9evXpZuXLlbNiwYWnLVxmVfgJp8eEt/RhUAxEQAREQAREQgeIgcMgXX9n0latsjYvhn+NO+NCsn+zz/fa2nBzWNl8mTZpkRx11lNWqVWuTsUSbn6uOzBYCUniz5UqqHiIgAiIgAiKQIQS+WrrMFsaJ3vSl2z7+z+WRUgZuac7qNXbxhInWpXatyPZwYdtKlWyfWnkHwof7Yn9XrlxpL7zwgi1cuNCee+652N1aL+MEpPCW8Qag6ouACIiACIhAugk8MftnG/nbkpSy/ScI7HXn4sBfrKDspqrw7rXXXv7wwYMHx2ajdREwKbxqBCIgAiIgAiIgAmkl0KN+XWtebatN8hy37A/7xv2tj9qDI0O7GlvbXjW3idr6n8VGVapssk0bRGBzCEjh3RxqOkYEREAEREAERCAhgUQD0dZt3GgNh4+0cs5fd4Oz7FZ0v+vc79t7tbfyuWmZCythmbSjbBOQwlu2r79qLwIiIAIiIALFRqCiU2oXHNbd7pw+0yavWGE7bbmlXdGsqZTdYrsCZfdEUnjL7rVXzUVABERABESg2AnkOqvutc2bFft5dcKyTUBxeMv29VftRUAEREAEREAERCDrCchhJusvsSooAiIgAiIgAiIgAmWbgBTesn39VXsREAEREAEREAERyHoCUniz/hKrgiIgAiIgAiIgAiJQtglI4S3b11+1FwEREAEREAEREIGsJyCFN+svsSooAiIgAiIgAiIgAmWbgBTesn39VXsREAEREAEREAERyHoCUniz/hKrgiIgAiIgAiIgAiJQtglk5MQTI0eOtPfee886d+5sffr02eQKTZo0ycaOHWsTJkyw8uXLW8uWLW233Xazdu3a2eLFi23QoEE2bdo0q1OnjjVt2tT69eu3SR7aIAIiIAIiIAIiIAIiUDYIZNzEE6eccorNnTvX1q9f76/APffcYx07dsxzNW677TYbMWKE5f5/e2cCb1PZ/fFlniuZh8yKEhqUKRkzlEwRkUp/M72lvIoXeStFqMhMikaRIS9FiAzJlKGEJDKTOZn3//mt///Z777nnnPuce+53HPOb30+9+69n/2M3332OWuvvZ71mCUKHbMGN/6gHL/xxhvSvXt3VYTTpEkjly5dkvTp08vChQvjlOcBCZAACZAACZAACZBA7BBIUS4Ny5cvl99++00VVVhuYaF955134l2N7du3S4YMGWTJkiUyevRoSWWWKcyRI4fmg9X3pptukm+//VbPXbhwQU6fPh2vDiaQAAmQAAmQAAmQAAnEBoEUpfCuXbtWMmXKJO+9957ceOONkj9/fnVR8L0UsOwWK1ZM5syZI71795brr79eDhw4IOfOndOsONezZ0+19EIZXr9+vW8VPCYBEiABEiABEiABEogRAilK4YUrQ758+dTvFvyh/MJC6yuHDx9WRXjNmjWq5B4/flyyZMkiW7Zs0awbNmyQjBkzqi/v5cuXZc+ePb5V8JgESIAESIAESIAESCBGCKSoSWtwY4CCagU+uLDm+krevHnlqaeekrffflvSpUsnhQoVkj///FP3kXfGjBk6mQ2uDPXr15cjR474VsFjEiABEiABEiABEiCBGCEQX5u8hgOHdffUqVNuD6CwwsrrK5kzZ5bPPvtMrbzw4c2WLZtOXIMiDPn77791mzVrVt3CrYHinwAm/1le/nOEJ3XBggXy119/JViZdUtJMGMSM9h2fvrpJ4FPOIUEUhqBWL83jx07pvM0Utp1YX9IgAQik0CKUngfeeQROXr0qPzwww+qwO7cuVNKly6tZBGq7JtvvtF9KMWYnFa2bFnNt3XrVmnQoIFkz55dz3fu3Fm3//znP3Xbvn173fJffAIjR46UkydPxj8R5hRcu4QUXkxQ/P7778PccvzqvO38/PPPVHjjI2JKCiCQku5NzK8YMmRIslPxtgOFd+nSpcneJhsgARKIDQIpSuEtUKCA1K1bV55//nmBny5Ck73++ut6JbAdOHCg7sNnF64PUHofe+wxzffrr7+q+8ODDz4ou3btkvvuu09WrlypW0R0iDaBhRIRLawSidBs+IGw4dwwXnt85swZ9XU+ceKE30mAXjYog1jGXkE51Ltv3z432bd990SAHUwuRCQN1APLPdxVfv/9d7f/qM8enz9/XmvBmPbu3RtSf/CghM8AxuorGI+N1OHbzkMPPSQ1a9aMU+SPP/5w+4UTgfpsC4GLN5SeTec2Ngn4uzfw+fN+NrGPP/vZwmcenyGvS5cvPcxnwHcbylhBHSiLz6BN99e+ze9vm9C9iT7Ze9N+36AefA/DKGHbRZq/7wq8QdqxY4df1zLvvenbDlzVnn32WVTriu/3k+Xn+31iC8A4Euh7webhlgRIIDYIpCgfXiDv06ePPPfcc+ragLBkVhYvXmx35YsvvtD9Q4cOCdwW4OJg5cUXXxT8YWGKChUq+PUBtnkjdQsryIABAzRSBazb3bp1Eyj6w4YN04gVL7zwgnz99dfy8ccfy7hx42TChAn6g4OHCCj/UDwRsxg+01bg54wHCvxg4YcMkS6QB/GM27ZtK8WLF9fFPJCGH15/7du6/G3hcz18+HCtG/3EjxtcUfBD+O9//1uvN36Y0D5cU0qWLKkPPvYHDe2jf3BP8fYHMZmnTp2qSjGidWDiYt++fTUuM36ce/Tooco+fMHLlSunbwW87axYsUKuu+46rXPZsmUyduxYueGGG2Tbtm3SqVMnadKkiY7bX5+x0Em/fv1U2UAdiBSCfNa1xh8HpkU3gUD3Jt5ajRo1SiZPnqyK7tNPP60P87gPBg0apEojotLgfsDchCJFisQBBWvvd999p59N3MeIT457FPc3lGB8Xh944AGpWrVq2O9NtDN79my9N+FK1q5dOxkzZozMnTtXChYsqMaJoUOH6lwK772J7wooylOmTNG+4m1KtWrVNIKOv3vz8ccfj9POvffeq1blSZMmqbLs7/sJCwwFujfhRjV+/Hj3u6tLly5Sp06dOFx5QAIkEEMEjBWNEmEE/vGPfzirV6/WXpvJek6rVq0c88PpGGXRMQqaM2/ePOfhhx92jAVY85hX+I5Zbc4xiqrmQ/lp06bpOWPhdIyi5rz88suO+dHSNOTr2rWrYx4a9Bh1TZ8+XcsiIVD7mjnAP/TLWE6dTZs2OeZH2e2bWRXPMQqqljKh5BwTP1n3Z86c6QwePFj3jdLrGCXe2bhxox57+2OUV+eVV17RdPz76KOP3Preeustx/xI6jmMySi/jlEWHG87I0aMcD744APHWMoc83bBWbduneYHE7BBuUB9Nq4gTu3atR1jwdIyRinQvHrAfzFJINi9gc+pUc4cY7XUzykA2c+WsZQqLzPh1jGKme7be9PEG3eaNWvmfs6M0ukYZVLz4DNuFtvRe9NYOZPt3sR3Sv/+/bVN8zbFadq0qWMsyXr85ZdfOviOgXjvTXwn/etf/3LMWxo9Z6zQjnnz5qCfge5NbztmRU393kLhQN9Plp/9rvN+n+BaLFq0SNs2irF+h+kB/5EACcQkgf+a+GJIyY/koeKVnlH8dKU5WHEheG2HyVdlypSRXr16CSy8eBVYtGhRd6iVK1d2LbpYuQ75zY+oex7HL730kh7D8nv//ferz/Q999yjaeXLl1frakLtuxUG2YEF1PYNi4T4i5NsLfqw4ELQLhYTuf322/XY9geW39atW8unn36qvrhYdtpax2DtbdOmjebHmGCFCiSwkmFVPtQLwdsF9A2h7/AWwV+fYaGGFap58+ZSqVIltV7hGlBik0BC9wbeXOHzCKuoeUh1IeFzZj+zuE/NQ5i6/NgMuDeRjlCLELjgwJIK1y8I3lzgzQfezAT7btDMCfzz9zn3LQJLM+4nWJkhZ8+eFVhvscolxN6b2Mc8CiwQhLcwmBxqfmX1DdGV3JuoJ9D3k1Gw/d6bKIMIPbAKG4VcLd+07oIKhQRilwAV3gi89vixadiwoavANm7cWOD/DMEPSc6cOVVR8yq03mHCp87+eNp0lDHWTHuofoHwi7PijZYRrH2bP9gWr/9DESjb+DG3AgXTiu2PsciqS0TLli1VgUd+rNgHgYuD178QLgeB2sb48XoZf3DjgMAX0jIIVO7VV1/V17aYXIPJcPhRh/sGJTYJBLs39u/frwoffMQRRhGfOV/BvYkHL/sZxPlcuXKpq4PNC59d/NmQjfZewPlg7dvywbaBPufeMlBaobTje8ef2P5AEYbrBpZ9x4Mh5lvY76QruTfRRrDvp0B9xnwQPIji+wDGAfPWSF1K/PWZaSRAAtFPIEVNWot+3EkfISJR3Hbbbeovii0skbBiQDGDlQVf6vBbg58fLBtW4KuKHyD84QfgjjvusKd0C6sRImFAQcREGFhTUb+vBGvfN++VHuOHHkomBP3BeG655RbtB/yRocz7Ciy68KWFwluqVCmdqGiVXFipoYiCDRQEWKBgBfO2Y+vDDyomyVhlGRMC8Yf2AwkmysFih2sAixv8fTHxiBKbBILdG/hcw+/dvGZXpQ+TcKE4QqAAw68cgmgmvvdm9erVdU4CLMgQhCtD9Bqr8Gqi+ResfZsnsVvvPYP7CnMHEEYS3xHo/yeffKJWZm/9+A6CT75xj1LF077Jwf14Jfcm6gz1+8nbPibjwTIMSy/efOGBw343ePNxnwRIIDYI0MIbgdcZk0ZefvllnZQGi+Sjjz6qE/eMj6D+oEJ5w5c9JrPZH0/8YOHVPwQ/Nr6v93AM5RB14QcJUS4CWXD8tY+JcEkVWGcR+gjKAX7g8CoUoeowiQwuEMZfNl4TsOJgrPhRhYUa40U5CMaJiStQhmF1gnUJE4O87XgrRPg6KCWY5Ac3ETAGS1iG/QmWv8ZEIVixsNIfHiYwAY8SuwQC3RuYsInPcI0aNVTpwmfU+MXrQxo+Y/isQQGG+wwme3kF5zEpFZ9j3GfIY119vPmwH6h933xXelyiRAm9NzEhGP3D9wTcMuCOAWs00n0F53AP4v5An+G2gfsPK18GujehkOI7APVhEpuVQN9PeCgOJOBlfIUFk96gfOOBFxZwCgmQQGwSSGW+ZP/PzBCb44/oUcPiA6tOQoIfWyhnLVq00Ff2vu4M3vJQ9HAeK9glJKG2n1A93vNQdtG2tV5hNjd+pBIKLQdLEl5t+ltkBGNCJA/va2Lfdrx9uNJx4RZCLGO8pqWQAAiE+hnCGwooZRMnTtSHLK/bji9JPNDhoSpYHlsm1PZt/lC2eLhGH+y9COUULhgJ9QdvjHDv2XLetvzdm77t+OYP9fvJlsO9CYXbfqfYdG5JgARiiwAfdyP4eoei7HqHBytvQpLQj5e3vLd9WFrwmtOfwH8Plp1QxPdHEZbTUCSYsulvTL7teNvwjsubHmgfSnaw9gOVY3r0ErjSzxBI+PucegnhQTCUB1GU8bYPhW/hwoXeqtx9+OIifGMoAoXRe9/gQTShPqNeb9hI33b8lfdtx1vGX37veX/7gXx8/eVlGgmQQPQSoMIbvdfWHRlWoQtF2XULJGIHLgNwPfAnof5I+yvLNBKIZgJ47W9XhkyucUKBDHRvBlNGk6s/rJcESIAErgUBujRcC+pskwRIgARIgARIgARI4KoRYJSGq4aaDZEACZAACZAACZAACVwLAlR4rwV1tkkCJBB2AvBVpZAACZAACZCAPwJUeP1RYRoJkEBEEUAIPkQjoZAACZAACZCAPwJUeP1RYRoJkEBEEVi1alVE9ZedJQESIAESuLoE0piA5y9f3SbZGgnELgHEMe3UqZMuQoCFQkaPHi2HDh2SMmXKuJE0YKlEHNVRo0bpUqg4hzBT48aN0wUHsOocVo3CCnOIb/r9998LVu6qV69enFijzz77rMYmRiQAvO5He4MHD9ZlVhFGrVixYnohUP6DDz7Q8FF9+/aVsWPH6mIbCCdnY5cibvGwYcPk7bff1pixq1evlltvvVVn/9sxoZ+DBg2SN998U1cGwyIg3jBSKDN06FAZOXKkHDlyRBciQHxUSLD+aYYg/1DnnDlz5ODBg7pkL8LjYWEHLCtrBSvg9ezZUxc8sCsRogyWht64caMulYslfK38+OOPAhbvvvuurF27VsqWLRtnLDYftyRAAiRAApFBgBbeyLhO7GWUEMAqdljJDStHIWj/ww8/rErVE0884S41i6VjO3ToIGvWrJHTp09r0HysoIXVp0qWLKmKLla7wpKpWPSiePHiupy0N9YqlkiGYolzWLzjrrvuknnz5kmjRo10IQ+s3DVlyhSlun37dlVksbIVlOOKFSvq6nXe1bywot2nn34qtWrV0nYXLVqkq+FhkQA7JoS/g9KJMaEvWIXOCiywKIvlmrEE8xdffKEr3+F8Qv2zdQTaYklpxGsuUKCAKuFQorH6ntenFwo9lFjEZP3666/lySeflM8++0xXL8PiD1gBbe/evdoExgZlGeybN2+uyjsU3n379gXqAtNJgARIgARSOgGstEYhARK4OgSMkouVDR2j4LoNGguipi1evFjTjCLr3HzzzY5ZyUqPf/jhBz0/a9Yst4xNmzFjhqY1bdrUMQqre75jx45Ow4YN9XjgwIGOUQid48ePu+eRlidPHscorM7kyZO1fmPpdM8bhdupWrWqHhtrrGMUP8csLuKenzt3rpYxyy47dkzGIuqeN0s663mjJGoa6jLLP7vnzVKvjlEynS1btjgJ9c8tFGTHWLsdY8HWHOiPiTvrvPfee24J86DgmAcAPX7ooYcco/g6xjLtnjcPBk6XLl302FimHaOUu+ewgzRv/+Oc5AEJkAAJkECKJ8CFJ1L6Ewn7F5UEYJ21glf/eJ2+bt06qV69uibDImuXQl6/fr2ucAULqRW4M+TNm1fgJtC4cWN56qmnpFWrVmKXcZ06daq6HiC/UY41L9werMCaCWvsnj17NAkLh9x+++32tBQqVEjLISFHjhyC+mAhff/993VFvaVLl2peWKntKnP33HNPnPI4gPXWfAtq2RdeeME9nzNnToElFZJQ/2B1vhLB0rNg8eGHHyqXlStXClwaWrZs6VZTu3ZttXTbBFij4boA140NGzZIvnz51KJuz+NawOJOIQESIAESiEwCVHgj87qx1xFOoHDhwu4IsDQxVsLCK3QrUDKtGMusnvcus4wyuXPnVncC5IP/Ll7lGyuw+gJDQTOWTK0C/sBYUcv64yIRSuRLL73kpvmuuIW8UFQhZ8+eVaV6xYoVUrlyZalSpYq0bt1acOwVb/9sW6gDSi/G5j3vLRdK/7z5Q9nHAwBcM6DYw3XDWLvlxhtvdIsWKVLE3ccOzqGfcIOAmwZY2jHgPFxQvMv1Io1CAiRAAiQQOQSo8EbOtWJPo4gArJtQyCCwPsKP9s477/Q7whIlSqg1FhbW8uXLax5MWsNkK/iqQtKmTStt27ZVv1Tst2nTRuySzig/f/58naBllbgdO3YI/Hy9E7W0Ij//Zs6cqT658L+11lakQaAcJiRQHqGcYzIZLKsQlGvRooV0795dkto/f+1XqFBBSpcuLdOmTVN/4YkTJ8bJZq3LNtG4YCh/8ICfb/78+cW4WtjTys/ydBO5QwIkQAIkEDEEOGktYi4VOxpNBIzfrL7Kh1sBogFA6atWrZrfIcL9ARbhfv36qWIMN4RevXqpEuktA6smFFvjX6uTsmxlxp9XXRcGDBig0R+gLMNCiygF6dOnt9kCbuE6gYlp6Ctk165d0qdPH92H9TcUad++vQwZMkT7d/78eRkxYoQsW7ZMoJgmtX9oHxZx4w+s0Stsf8ADk/tgDcekO6/AbWHSpEk6cRBbPExg4iCkc+fOahWePXu2jhvuG5jsh8gSFBIgARIggQglkOK9jNlBEogiAnaCl7HAOsZv1smQIYNj/HUdY/10R4lJa2YhBfcYO5hQZizAjrHQumU2bdoUJw8OjNVYJ1j5njChzBxjZdWymLCFSW6YcAbBpDWjMMYpgolk5cqVc9OefvppnehllF/HWHkdE7FBJ4YZP1l30pqxmrr5jcVaJ61t27ZN04xvsYOJcMbVwjE+to5xjXAw8c1KsP7ZPMG2JrybY5R3p2DBgm42jM9Yux3jO+ymYQeT1jBhDhPVUMZYcx1jAXbzoK+YoGYsunqNjPuD452Q52bkDgmQAAmQQMQQSIWeRqiuzm6TWgd1GAAALpJJREFUQMQRgEUUE8TwSh2+sCdOnAjJrcAO9OjRo2qxTKw/KXxa8do+FMuubdNuYZmFlROv+xMrmBQGn2QTIcJvFUntn4lsof7KqBzxjeGCAWsuQpdZgT8v0hHnGO1hPLAC+wriC8OqbZRo31M8JgESIAESiDAC9OGNsAvG7kYPASidofjQekfsnXjlTQ91H7FqEyvob1KUXbRrLNoBlV2c9+0ffH0T8hOGzzIE/cMfJp798ssv6jYBlw+vsqsZPf982/OcUh9oKrteItwnARIggcglQIU3cq8dex6BBGBJTKyFNQKHm+QuYyHIMWPGBKwH0Sjgk+wVWKExIdBOhvOewz4iYnhXgPM9z2MSIAESIIHoI0CXhui7phwRCcQ8AbiOIB4vhQRIgARIgARAgAovPwckQAIkQAIkQAIkQAJRTYBhyaL68nJwJEACJEACJEACJEACVHj5GSABEiABEiABEiABEohqAlR4o/rycnAkQAIkQAIkQAIkQAJUePkZIAESIAESIAESIAESiGoCVHij+vJycCRAAiRAAiRAAiRAAlR4+RkgARIgARIgARIgARKIagJUeKP68nJwJEACJEACJEACJEACVHj5GSABEiABEiABEiABEohqAlR4o/rycnAkQAIkQAIkQAIkQAJUePkZIAESIAESIAESIAESiGoCVHij+vJycCRAAiRAAiRAAiRAAlR4+RkgARIgARIgARIgARKIagJUeKP68nJwJEACJEACJEACJEACVHj5GSABEiABEiABEiABEohqAlR4o/rycnAkQAIkQAIkQAIkQAJUePkZIAESIAESIAESIAESiGoCVHij+vJycCRAAiRAAiRAAiRAAlR4+RkgARIgARIgARIgARKIagJUeKP68nJwJEACJEACJEACJEACVHj5GSABEiABEiABEiABEohqAlR4o/rypuzBHThwQD7//HPZsGFDyu4oe0cCMUpg+vTpcvHixRgdPYdNAiQQTQSo8EbT1YygsSxfvlxq1KghGzdulMcee0wmTZoUQb1nV0kg+glMmDBBOnXqJBcuXIj+wXKEJEACUU8glWMk6kfJAaY4AtWqVZNBgwZJpUqVZM+ePVK7dm219GbIkCHF9ZUdIoFYIgCLbrt27eTQoUOydu1a2b17t2TKlCmWEHCsJEACUUiAFt4ovKgpfUj4Qf3tt9+kYsWK2tWCBQtKtmzZZOfOnSm96+wfCUQ9AVh08QA6Z86cqB8rB0gCJBA7BKjwxs61TjEj3bt3r1x33XWSKlUqt0/Zs2eXw4cPu8fcIQESuDYEYM1t27atpE2b9tp0gK2SAAmQQDIQoMKbDFBZZXAC+CH1nQiD48yZMwcvyLMkQAIkQAIkQAIkkAgCEaXwzpgxQxo3bix9+/YNOtTVq1dLt27d5LnnnpNTp04FzcuTV59A7ty59bqcPXvWbRz+goUKFXKPuUMCJEACJEACJEAC4SIQMQpv586dZdiwYXL8+HH59ttvpX79+n4Z9OjRQ/C3efNmWbNmjU6+8JuRideMQLp06aRmzZoyefJk7cPcuXMlV65c+nfNOsWGSYAESIAESIAEopZAxCi8UGDLly+vym7Hjh3l9OnTOrvfe2XwWhzW3SxZsqj/WerUqQWxXmE9pKQsAi+//LKMHz9eJ669+uqr8s4776SsDrI3JEACJEACJEACUUMgIhReWHUhsPBC2rRpo9uPPvpIt95/UHILFy4sLVq0cJMR9oqSsgiULFlSH07+85//yIoVK6Rs2bIpq4PsDQmQgE4kZUgyfhBIgASigUBEKLzTpk1T1ngV7hWEtvIKLLmI41qiRAnBCkGXL1/WiVAMNeyllLL2c+TIkbI6xN6QAAmQAAmQAAlEHYGIUHgDWRgQ2sor+/btk3PnzglcHmDlRdirM2fOxAl/5c3PfRIgARIgARIgARIggegnEBEKb926dfVK7Nq1K84VqVWrVpxj+O/CqtuwYUPZunWrWMvuunXr4uTjAQmQAAmQAAmQAAmQQOwQiAiFN2fOnHpFOnTooNtWrVrptl69erodMmSIbNu2Tdd9hw9v0aJFZcCAAZImTRo936hRI93yHwmQAAmQAAmQAAmQQOwRiAiFF5elSZMm6p5w3333aXSGGjVquFdr1qxZMmLECPnll1/Uwrtjxw6N1Xvp0iXNs3//fjcvd0iABEiABEiABEiABGKLQCrz2t+JpCEvXLhQfF0Z/PUfy9cWKFDA3ymmkQAJkAAJkAAJkAAJxBCBiFN4Y+jacKgkQAIkQAIkQAIkQAJhIJA2DHWwChIgARIgARIgARJIFgIzZswQxGzPkyePvPbaa0lu4+TJk+Ib5Skxlfbt21ejQuXLl0+wgFLbtm11DlEodcHlEqvBYuXYn3/+WQoVKiRPPPGEhlUNpXxCeTZt2iTz5s2Tf/7zn36zHjx4UMaMGSPPP/+8ZM2aVYYPHy7Hjh1z815//fVSsGBBefjhhyV9+vRueig758+fV/fSjBkzJpgdgQaeeeYZwVysUPInWGGQDBHjwxtkDDxFAiRAAiRAAiQQhQTWrl0rzZo1Eyip+fPnT/IIu3XrpspdUiv6/vvvZePGjaoUQnmFwrtz586Qqr1w4YLOS8KcpK+++koQevWTTz6RUqVKyeLFi0OqI6FMUHihRAYSrEKLFU+xai3k7bfflkmTJsnSpUtlyZIluhIqFPDixYvLypUrA1UTLx1KMxaS2r17d7xz/hIQaKBIkSJhuSb+6vem0cLrpcF9EiABEiABEiCBFENg8+bNki1bNvn0008FylFSZdWqVRKOyE0vvPCCvPXWW9odWEChxIYqWC32hx9+kN9//z2OEg8L8dNPPy1QVrNkyRJqdX7zPfbYY4K/K5HWrVvHsaAfOXJEGjRooKvbbtiwQS3BCdWHlXERFvZKpGvXrnLbbbfp2JNzMaqkf3quZFTMSwIkQAIkQAIkENUEsArqs88+Kwgd2rRpU3nzzTcFr7mtrF+/Xp566imdgN6+fXtV/uw57/aLL76QiRMnCiyoWFDKWhq//PJLadeunZaHkohX916BovbGG29oTP4XX3xRYCWGDB06VJVMRHZ6/fXXNQ1149U+IkHh9f2wYcPiKK941Q8rbI8ePQQKIZQ59AsuARUqVNA6Ll68KBgHIkVBoPT16dNHHnjgAXn00UdlwoQJ7roAUHKnTp2qFlRfizUU6Dp16mgfUU+wvsHCDEURebwC7itWrBAo9ti3Av7vvvuuKvudO3fWaFf2XKAtQsLOnj1bcD0xBiuBru9ff/0lL730kmb717/+JQsWLNB9LAg2ePBgad68uWBdBbgweNdVgIW7evXq8sorr9gmkmVLhTdZsLJSEiABEiABEog9Anitj1faR48elccff1xKliypigwUIAgURSg38NeEkogVUatUqeIqi15iN910k77uhgX1nnvuEShgI0eOVMtlsWLF1OcVq6nCComFpyBQuurXry9TpkxRhRvKFupHv+AyAMspIjjdeuutmh+KM5Ri9PPuu+9WRRnlbQArKLtYAwD+tnj9D39XKKxeKzH8UKEQIjoUBBZcuCbAwgqluGfPnlovzkFRxRoBUGx9BdbNsWPHqrUT54L1Da4G48ePF0SusrJ8+XLlg3O//vqrMrDnoBzD7aJy5cr68AHlPRTJmzevXgNYnSHBrm/atGmlXLlymu/2228XlIVAyYWFHhG2wHbRokVSs2ZN9fPVDOYfHjbANVkFYckoJEACJEACJEACJJBUAsba6hhFzTGWR7cqY811jL+qHn/zzTeOUYwco/jqsVEWHWNVdczELTe/d8codY6ZPOUm9evXzzFKoXtsXAmcG264wTGWWE175513HKMoO0bRdfN06dLFGTdunB4bpdYxlkTdN24FCMvqGIuvm9emmYlymmYUNOfmm292jBXXzVOmTBln9OjR7jHaQj0YGyR79uyOsRq7541F2pk5c6YeG+XXMT6r7rlAO7YfwfpmrOeOeahwqzBWcMesNKvHH374oXPjjTfqvnFHcMyDhfPTTz+5eY21W/ts1inQNLNgl9O7d2/3vHcH7VStWlWTErq+xvqr9RpLuOY31nbHWHbjXN+5c+dqHuNH7DaDvoGheVBy08K9Qx/eZH2cYOUkQAIkQAIkEDsE4MYACy6seFu2bNEIBEYRdK199957r06EgkUVr/xh8YNbQqi+m1hFFYtJwa0ALgTwLf3777/l7NmzChnuEpgM5o0sAKuwP0HeDBkyxIntDysvLJOwGDdu3FiL3XXXXe7KrXBfwMqusD4HEkz2Mkq2WlhhfYY1GD6qELgxHDp0SK2bwXySQ+kb3EKw8iys3LAaw0IKFxBf+fHHHzXChbVq4zyuEyI0hCKYMIioDZCErq9vfbiu6Bf68P7776tLCCbGQXDdrFieRvEVo1zb5LBu6dIQVpysjARIgARIgARilwAiFxhroU5Agh8p3Bvuv/9+FwhcAjBhCz60UNSgGMI9IdToBIgmgPpRHorjI488Irlz53brh8tEqBO+4GtrrMNx8sPFAvV5fWO9yjgUa0xQCxZCC7648BO+5ZZbNPqAsQir2wQ6eeedd+q44cvrK8aiqWHE8LAQSt+gfIIn2pozZ44qvQ899JBvtVoX3C5QvxW4H4QiKAO/ZbhJQBK6vr51ghf6Wa1aNXVryJw5s/pC++bDAwrYnzp1yvdU2I6p8IYNJSsiARIgARIggdgmgFBX8JXFxCbzWl0wQQrKllUgYR3FpDMouoitC6W1dOnSOqEqIXKwCPbq1UsnwcECC+UXk+KsxRTloTz7RgnARDT4r/pKiRIl1KcY1kcrsB5DqbvjjjtsUpwtFEyU823DZoISj/BesGTD2rpv3z4BE/QByh8U3ly5cvmdoPXxxx/r2A4fPqxtQHkP1jcorbCOf/bZZxrWDL7D6dKls11xt2gTjLx1eX1/3Yx+dnAN//jjD/VLxmmMJdj1hdIKscq1ceVQP2NYbuEPjdjFdrIePhdWtm/frmUCcbf5krKlwpsUeixLAiRAAiRAAiTgEoA7ACasQTmF0gPr47Rp0wSTx6w8+eSTmgYlGK/LEbvVWhDhpjBo0CC1Str8dgsFD9ZWxJCFsgTlEjP+Ubd1aUBYr2XLlmnIMExgg5KH2f9wVYCgPFwtoNjCnaJw4cJi/IIFCteePXtUoYaFFxbJQAIFMpDCi4gDo0aNUosurLTgAAUWE+VgFYbC/Pnnn+vDQPfu3dV1AjFroRAj1BncMaDEh9o3uDXMnz9fjF+sgKs/wdjhUvHvf/9bxwiF3vg0x8sKxfa7777TWLzTp0/XiAudOnXSyYE2IkVC19f4DWu9iIxx4sQJdQ/BdYbyDkF0BkSwgNhrhn3whCJsJ7ohLexiPpAUEiABEiABEiABEkgyAaM46gQ18+paJ02ZmfkOJpIZZc8xCpDWbyyzjnnd7xjXA53AZhaWcM8Zf0+dvLRjxw7N6ztpzURfcIwV1zGKq2Pi8zpmJTGnZcuWjvGTdftuJ2wZ/1ydwIaJblYw2cy8PncnwhnlzzEKrGP8aR3kN/66jolIYLM7mLRmFqtwj7FjQmw5NWrUcNN8J62ZSAyOceNwwABtYaxGAXTzY8covY6JXuAYBVjHayyzjolaEWfSVkJ9sxVWrFjRMZZRe6hby8AmmugKjol0oWPEODFBzSiUjnfSGo7tn/HZ1TqNe0acCXuhXF/jwqD1mFBu2rx5CHHMynaOUWb1epiIDTrREH200r9/f3fCnU0L9zYVKgy7Fs0KSYAESIAESIAEYpbAn3/+qRPHsGhEIIGlFj60wfxhA5WFNRbWwEC+qFBtECYMllX7mt3WhZi0mHwGf1IrsEojn4mwYJMCbmEdxmt9+CLDTzeQIIwZLLxwYQgk6Acsq1jG1587AspdSd8CtWPTYW3GNUkMc1sHtgldX/jigi8m00HAHPGRrTuDJv7/P/hEw8KPeMiY5JdcQoU3uciyXhIgARIgARIggagkMHDgQDFWaL9REaJywMk4qA8++EAntPkuIBLuJqnwhpso6yMBEiABEiABEohqAvA/xSILiKgAKzIlcQRgicdENUzY84ZNS1xtwUuFVeGFmR9O4r5eEoi3RyEBEiABEiABEiCBaCGA1/YIp4VYvpTEEcDkQ0zus5PdEldLaKXCovBC0UUA6c2bN/tt1VcB9puJiSRAAiRAAiRAAiRAAiSQDATShqPO1157TR3AFyxYIIUKFZJgq4eEoz3WQQIkQAIkQAIkQAIkQAKhEgiLwov4dSZsh9SuXTvUdpmPBEiABEiABEiABEiABK4KgbAsPAGHY6x6QiEBEiABEiABEiABEiCBlEYgLD68WEIQsdNatGghJrBxnHWpMWAToDmljZv9IQESIAESIAESIAESiBECYVF4O3ToIGY1lIDIOGktIBqeIAESIAESIAESIAESSGYCYVF4sUoG1koOJEld0SNQvUwnARIgARIgARIgARIggYQIhEXhRSOIpYYl67CMnj1GYOb169dL69atNY3/SIAESIAESIAESIAESOBqEwiLwrtr1y6pWbOmwJfXV7BO9rFjx3yTeUwCJEACJEACJEACJEACV4VAWKI0DB06VDJlyiRz586VypUry8iRI2XGjBm6TNyYMWOuykDYCAmQAAmQAAmQAAmQAAn4IxAWhXfr1q3yP//zP1K/fn2pVq2a/Pnnn9K4cWNVgHv27OmvXaaRAAmQAAmQAAmQAAmQwFUhEBaF97rrrhM7Me2WW26R5cuXa+cLFy4smNC2b9++qzIYNkICJEACJEACJEACJEACvgTCovBWqFBBxo0bJ1u2bJHy5cvLihUr5I8//pA1a9bImTNnJGfOnL7t8pgESIAESIAESCBGCVw+eUIu7dwpl0+cCCuB/fv3y4cffijr1q0La72sLPIJhEXh/cc//iGw8vbv318VXvjxFitWTBehaN68uaRPnz4spOAXDFeJvn37Bq0Pq75hqePnnntOTp06FTQvT5IACZAACZAACVw9AhdWLpczvXrK30MHyZlnusj5BfPD0vi3336rOgiU3QcffFBGjx4dlnpZSXQQCEuUBosCvrs5cuQQhCObOXOm5M2bV6pXr25PJ2nbuXNn2bx5s6RJk0Zj/mbNmlXmzZsXr84ePXroMsc2H/rw+eefx8vHBBIgARIgARIggeQh4Jw/L+bHOl7ll3b+JmcHDYyXnuHZ5yVtqdLx0s2PvqQK0WhWtmxZnTR/3333ye7du+Xuu+/Wt80ZMmSIXy9TYo5AWBXe5KSHDzDcJUaMGKGvK8aOHSuffPKJFCxY0G324sWLuoxxlixZBPvwH0Z84OnTp0vu3LndfNwhARIgARIgARJIPgJ/vzVELv24PskNpLm1jGTq9VKC9eA3P1u2bOpGmSpVKs1fokQJmTVrltx2220JlmeG6CcQFpcGi2nJkiWCEGVfffWVxuQ9cuSIPZWk7fHjx7X8sGHDdNumTRvdfvTRR/HqTZ06tWCyXIsWLdxze/bscfe5QwIkQAIkQAIkkLwEUpu5O6kLFIz3J+btrF8xhip/+VPlyuU3u28i5g1df/31YpVdnMcb54MHD/pm5XGMEkgbjnFjWeFmzZrpkxRcDbp3766RGXr16iWTJ0/WcGVJaWfatGlaPF26dHGq8V3o4tChQ4JXF3iqg1UX1t3MmTOL4zhxyvGABEiABEiABEgg+QhkePxJv5VfPnJYzrxowpWaN7BeyfzaIEmdPbs36Yr206ZNq292vYXwlhdvfCkkAAJhsfAOHz5cVq1aJdu3b5euXbsq2SeeeEI6deokr7zySpJJY1ELf4KJcl5B+LNz585Jx44d1cqLJz1EifA+8Xnzc58ESIAESIAESODqEUidM5dkfvV1MdYpMTPaJVWePJJpwKtJUnbRe8zXOXnypM4hsqM5cOCAFC1a1B5yG+MEwqLwLl68WBCpAZZVK5g01qdPH9m4caMuRGHTE7OtW7euFsMSxl6pVauW91Anq8Gq27BhQ8FiGNayy/AkcTDxgARIgARIgASuGYHUefNJ1nHvSdbxkyTL4GGSpkjSlVK8Aa5Xr56GSMXAMHE+j1GmOX/nml3mFNdwWBReuBH4C/+1Y8cOtbDiVUNSxMbx7dChg1bTqlUr3eLDDRkyZIhs27ZNLcrw4cUT3YABAzSiA843atQIGwoJkAAJkAAJkECUEhg8eLDgjTMWwOrdu7e89957UTpSDisxBJKmif5/i7CoPv/881KmTBk5b0KRQAHG4hP9+vWTihUrqiN5YjrnLdOkSRNBHF5Ea4DUqFHDPY1ZmLD+dunSRf12oWh7Y/UiEHWuEB3f3Uq5QwIkQAIkQAIkEDEESpUqJb/++qtgwrw1lEVM59nRZCcQtrBkcGl499131Y0Arxag+OIpC8ootuGShQsXiq8rg7+69+7dKwUKFPB3imkkQAIkQAIkQAIkQAIxRCBsCi+Ywa0AywkjjFjx4sWlZs2a4htZIYbYcqgkQAIkQAIkQAIkQAIpgEBYFd5NmzYJZkX6Sp06dXyTeEwCJEACJEACJEACJEACV4VAWHx4EYmhcePGsnPnTr+dttES/J5kIgmQAAmQAAmQAAmQAAkkI4GwRGmYOHGi3HrrrWrdxfJ+WIjC+5eM/WfVJEACJEACJEACJEACJBCUQFgsvIiCULt2bY15F7Q1niQBEiABEiABEiABEiCBq0wgLBZerKo2ZcoUwVrWFBIgARIgARIgARIgARJISQTCMmkN7gtYBOKbb77RWLzZsmWLM0bE5KWQAAmQAAmQAAmQAAmQwLUgEBaXhtdff11WrVolWAEN4cgoJEACJEACJEACJEACJJBSCITFwtugQQO59957pX///illXOwHCZAACZAACZAACZAACSiBsPjwYknhs2fPEikJkAAJkAAJkAAJkAAJpDgCYXFpaN++vdStW1cHd9ddd0mOHDniDLRGjRpxjnlAAiRAAiRAAiRAAiRAAleLQFhcGjp06CDjx48P2GcuPBEQDU+QAAmQAAmQAAmQAAkkM4GwKLwXLlzQhSYC9TVjxoyBTjGdBEiABEiABEggxgicMHrDwbPnJFeG9JI9ffqwjv7EiROydOlSadiwYVjrZWWRTSAsCm9kI2DvSYAESIAESIAErhaBufsPSo+NmyV1KpGj5y/I8HK3S4ubCoSl+TNnzkizZs0kTZo0MmfOnLDUyUqig0BYJq1FBwqOggRIgARIgARIIBwE4Mp42c/fpuMnpN3a9XLcWHih7EKe2bBJVv151G/+K3GJ3Lx5s5QrV06OHz8ejiGwjigjQAtvlF1QDocESIAESIAErjWBtj+slfmHDie5G/flzCGfV6wQUj0rV64UKMj79++XSZMm0cIbErXYyRSWKA2xg4sjJQESIAESIAESSIhArowZpFDmTPGyHTdW3ZMXL8ZLz5o2rdyYPl289DwZMsRLC5RQqVIlPTV9+vRAWZgewwSo8MbwxefQSYAESIAESCA5CAwtW8Zvtbv+OiPVliyTc5cvxzm/uFoVucmPghwnEw9IIAkE6MObBHgsSgIkQAIkQAIkEDqBwlkyy7yq/2eJzWwmluU3UZzmVLmXym7oCJkzkQRo4U0kOBYjARIgARIgARK4cgK3XpdN/mjwgDimqAnUIOlS0/Z25RRZ4koJUOG9UmLMTwIkQAIkQAIkkCQCVHKThI+FE0GAURoSAY1FSIAESIAESIAESIAEIocA3yNEzrViT0mABEiABEiABEiABBJBgApvIqCxCAmQAAmQAAmQAAmQQOQQoMIbOdeKPSUBEiABEiABEiABEkgEASq8iYDGIiRAAiRAAiRAAiRAApFDgApv5Fwr9pQESIAESIAESIAESCARBKjwJgIai5AACZAACZAACZAACUQOgYhSeGfMmCGNGzeWvn37BiW8evVq6datmzz33HNy6tSpoHl5kgRIgARIgARIgARIILoJREwc3s6dO8vmzZsljVmK8NKlS5I1a1aZN29evKvTo0cPgcJr8+XNm1c+//zzePmYQAIkQAIkQAIkQAIkEBsEIsbCC2W3fPny8u2330rHjh3l9OnTsmfPnjhX6eLFi6rsZsmSRdKmTSupzXKFBw4ckEOHDsXJxwMSIAESIAESIAESIIHYIRARCu/x48f1igwbNky3bdq00e1HH30U70pByS1cuLC0aNHCPeerGLsnuEMCJEACJEACJEACJBD1BCJC4Z02bZpeiHTp0sW5IL/99lucY1hyM2TIICVKlJDp06fL5cuXJXPmzOI4Tpx8PCABEiABEiABEiABEogdAhGh8GbKlMnvFbnuuuvipO/bt0/OnTunLg+w8qZKlUrOnDmj2zgZeUACJEACJEACJEACJBAzBCJC4a1bt65ekF27dsW5MLVq1YpzjMlqsOo2bNhQtm7d6lp2161bFycfDxJPAL7Uv/76a+IrCFJy/vz58tdffwXJEfwULPmzZ892r3vw3DxLAtFFIDnvTdS9ffv2JAHD9/PevXuTVAcLkwAJkEBiCUSEwpszZ04dX4cOHXTbqlUr3darV0+3Q4YMkW3btkmnTp10olrRokVlwIABGqkBGRo1aqT5+C/pBBYuXCirVq1KekV+ahg9erRYf20/pxNMwsPOm2++SYU3QVLMEI0EkvPeXLRoUZLve4SV3LJlSzSi55hIgAQigEBEKLzg2KRJE3VPuO+++zQ6Q40aNVy8s2bNkhEjRsgvv/yiFt4dO3ZorF6EL4Ps37/fzcud8BA4duxYHGsNImTAOnvixAlVWnHsjYF8/vz5ONZbnIOlGC4nvoLrdeTIEd9krXfnzp2Cur2Cunfv3k1F1wuF+zFLwPfeBAjcl7hv4PZlj/GAaAVlrAS7NxEdB2/avGVRDg+q/u5NnPvjjz/k7Nmz2KWQAAmQwDUjEDFxeC0hWDF8XRnsOe8Wr84KFCjgTeJ+GAi88847smnTJlVmEes4R44calWFhR3n8MOHkHAvvviijBkzRsaOHautLliwQBYvXiwDBw4U7I8fP16KFy+uDyldunSROnXq6EMNfK/xw4wfz/r16+sCIqgAdc2dO1cKFiwohw8flqFDh0qhQoVk6dKl8sYbbwis+njA+emnn2TJkiVq6Q/DcFkFCUQMgUD3ZsaMGfUtl73fcL/06dNHJkyYIHny5NH7pnr16np/4h71d28OHz5c760LFy5ofrgPjRo1SuOhB7o3jx49qvdvtmzZ5M8//5T06dNLu3btpHbt2hHDlB0lARKIHgJpI20ooSi7GBOV3eS7srDuICQcQsA988wz8uWXX8ott9wiiJoxdepUVYI3bNgQsAP/+c9/BAuJwEoPX2soqVaqVq0qjzzyiPz+++/Svn176dq1qyrRUJIRrQM/mnPmzJGZM2cKFOVXX31VFe3SpUurQuyty9bJLQnECgF/92bz5s11+Li38KCIybyBJNi9iXsPbke479u2bSsbN24U3Hf+7k18L0AhRpu4T2Fhbtq0aaBmmU4CJEACyU4g4hTeZCfCBhIkgB8xWHEhlSpVkp9//lkV3vz580uuXLkSLA/LLSy9UJRRF6y7Vu68807dLVKkiFqS4Cbx3XffaXvwz4Xg9SjabNCggYahK1WqlKZXrlxZt/xHArFKwN+9aVlg4Z5gyi7yBbs3UR7KLqRkyZJqtQ10b3bv3l3v0Z49e2r+66+/XsqUKaP7/EcCJEAC14IAFd5rQT3C27Q/ehgGwsDBzQDiGz7O+lDj3N9//42NCqJuQFFevny5fP3112qtnTx5sp5DHGUr9scZr0/RRuPGje0p3ULpRhuwasG9An/evsXJzAMSiAEC3s+/997E0APdn14/+iu5N3FfBro30R7ipsO/3op9SLbH3JIACZDA1SQQMZPWriYUthWcwIoVK9TNAErssmXLpFq1avEKwKKDZZ3tj+n333/v5undu7e6McCa1KtXL51U6DsRzc1sdu6//351fciXL5/cdtttOgnmk08+0RX18JrV1g3/Q9/JNN56uE8C0U4glHsTDBDDHH7yEHv/YD9c9yYeVu+44w7BnAvck5iE+uOPP6IJCgmQAAlcEwK08F4T7JHdKJTZJ598Un/IqlSpoq834c/nlWLFigncEx577DHBpBW8DsXEFUjLli3lrbfekkmTJukENLz+DGb9ueGGG+TRRx8VhKO76aab1JKLSXH4UX3ttdekf//+OqkNE3C8FmJvf7hPArFAwN+96W/cjz/+uPq/4565+eabBeUg4bo3UVfHjh1VgcZ3ACzBcIOgkAAJkMC1IhBxURquFSi2G5cAXlVixnaWLFninvA5QogjvEr1p9CePHlSZ3l7X8P6FI9zCCswrMpQoH0FdfmuvOebh8ckEAsEQr03cf/C7SFr1qzxsITz3sR3AL4nQr3P43WGCSRAAiQQBgJUeMMAkVWQAAmQAAmQAAmQAAmkXAL04U2514Y9IwESIAESIAESIAESCAMBKrxhgMgqSIAESIAESIAESIAEUi4BKrwp99qwZyRAAiRAAiRAAiRAAmEgQIU3DBBZBQmQAAmQAAmQAAmQQMolQIU35V4b9owESOAKCCCyAIUESIAESIAE/BGgwuuPCtNIgAQiikC3bt1k+PDhEdVndpYESIAESODqEaDCe/VYsyUSIIFkIrBq1apkqpnVkgAJkAAJRAOBNC8biYaBcAwkEAkEEOy/U6dOUrRoUXnllVdk9OjRcujQISlTpoxgmWQILJXHjh2TUaNGyeTJk/Vc9uzZZdy4cbqy3Mcff6zLMd9999266hyWhn399delXr16cYL7P/vss7oYB1anw+t+tDd48GD5+uuvdWUtrIYHQfkPPvhAMmfOLH379pWxY8fqstD33nuvWx8WKBg2bJi8/fbbMnHiRFm9erXceuutglXw7JgwhkGDBsmbb74pUECxtKx3kRCUGTp0qIwcOVKXmi1SpIi76EGw/mkng/xDnXPmzJGDBw/qan5bt26VJUuWSKVKldxSu3fvlp49e+oy1ePHj9cV/lDm1VdfFawSWLBgQcmVK5ebH8vggsW7774ra9eulbJly8YZi5uROyRAAiRAAhFBgBbeiLhM7GS0ELh06ZJMmDBB6tSpo6vGPfzww6pUPfHEE7r8Ksb51VdfSYcOHWTNmjVy+vRpVQrbtWsnWE4Zy7NC0X3jjTekfv36WqZ48eICJW7hwoUupuXLl6tiiXN//fWX3HXXXTJv3jxp1KiRrnr34IMPypQpUzT/9u3bVZHFcrNQjitWrKhLwmLZZit169aVTz/9VGrVqqXtLlq0SGrWrKnLS9sxNWjQQJVOjAl9eeCBB2xxVYBR9rfffpMmTZrIF198Ic2aNdPzCfXPrSTATqlSpXQlrwIFCqgSjpXDevfurUq+LQKFHkosVuODwo+lsT/77DNdSnfz5s1So0YN2bt3r2bH2KAsg33z5s2171B49+3bZ6vjlgRIgARIINIImDXOKSRAAleJgFka2THfEY5RcN0WjQVR0xYvXqxpRpF1br75ZscspazHP/zwg56fNWuWW8amzZgxQ9OaNm3qGIXVPd+xY0enYcOGejxw4EDHLO3qHD9+3D2PtDx58jiXL192jBVZ6zeWTve8UbidqlWr6vGRI0cco/g5P//8s3t+7ty5WubAgQOOHZOxiLrnFyxYoOeNkqhpqKtr167u+cOHDztGyXS2bNniJNQ/t1CQHfMQ4BgLtuZAf4zl2XnvvffcEuZBwTGWZT1+6KGHHKP4OsYy7Z43DwZOly5d9NhYph2jlLvnsIM0b//jnOQBCZAACZBAiieQNtIUdPaXBKKBAKyzVvDqH6/T161bJ9WrV9dkWGTTpEmj++vXr5cMGTKoddWWgZU3b9686lrQuHFjeeqpp6RVq1Zy5swZLTd16lR1PUB+oxxrXrg9WIE1Ey4Ae/bs0aRMmTLJ7bffbk9LoUKFtBwScuTIIagPFtL3339f4DKwdOlSzWuUS3WPwME999yjafiH8hBYb823oJZ94YUXNA3/cubMKbCkQhLqH6zOVyIZM2ZUFh9++KFyWblypcCloWXLlm41tWvXVku3TYA1Gq4LcN3YsGGD5MuXTy3q9jyuBSzuFBIgARIggcgkQIU3Mq8bex3hBAoXLuyOIFWqVOoLi1foVqBkWjGWWT1vrLQ2SVAmd+7cAncCCPx38SrfWIHVFxgKmrFk6jn4A8M/N3Xq/3owQYl86aWX3DSc9wryQlGFnD17VqBUr1ixQipXrixVqlSR1q1b67G3jLd/ti3UAaUXY/Oe95YLpX/e/KHs4wEArhlQ7OG6YazdcuONN7pF4T/sFZxDP+FLbKzeytKOAfngggI/agoJkAAJkEBkEqDCG5nXjb2OcAKwbkIhg8D6CD/aO++80++oSpQoodZYWFjLly+vefbv36+TreCrCkmbNq20bdtW/VKx36ZNG0mXLp2eQ/n58+frBC2rxO3YsUPg5+udqKWZ/fybOXOm+uTC/9ZaW5EGgXKYkEARh3IOyzAsqxCUa9GihXTv3l2S2j9/7VeoUEFKly4t06ZNU39hTLTzirUu2zTjgqH8wQN+vvnz5xfjamFPKz/L003kDgmQAAmQQMQQ+K/JJ2K6zI6SQOQTQPQFvMqHWwGiAUDpq1atmt+Bwf0BFuF+/fqpYgw3hF69eqkS6S0DqyYUW+NfK0+aSVlWjD+vui4MGDBAoz9AWYaFFlEKbGQIm9ffFq4TsCSjr5Bdu3ZJnz59dB/W31Ckffv2MmTIEO3f+fPnZcSIEbJs2TKBYprU/qF9WMSNP7BGr7D9AQ9M7oM1HJPuvAK3hUmTJunEQWzxMIGJg5DOnTurVXj27Nk6brhvYLKf8WX2VsF9EiABEiCBSCKQ4r2M2UESiCICdoKXscA6xm/WMb65jvHXdYz10x0lJq2ZhRTcY+xgQpmxADvGQuuW2bRpU5w8ODBWY51g5XvChDJzjJVVy2LCFia5YcIZBJPWjMIYpwgmkpUrV85Ne/rpp3Wil1F+HWPldUzEBp0YZvxk3Ulrxmrq5jcWa520tm3bNk0zvsUOJsIZVwvH+Ng6xjXCwcQ3K8H6Z/ME25rwbo5R3h0TXszNhvEZa7djfIfdNOxg0homzGGiGsoYa65jLMBuHvQVE9SMRVevkXF/cLwT8tyM3CEBEiABEogYAqnQ00hS0NlXEohkArCIYoIYXqnDF/bEiRMhuRXYMR89elQtlon1J4VPK17bh2LZtW3aLSyzsHLidX9iBZPC4JNsIkT4rSKp/TORLdRfGZUjvjFcMGDNRegyK/DnRTriHKM9jAdWYF9BfGFYtRGjl0ICJEACJBDZBOjDG9nXj72PYAJQOkPxofUO0Tvxypse6j5i1SZW0N+kKLtoF9EmAim7OO/bP/j6JuQnDJ9lCPqHP0w8++WXX9RtAi4fXmVXM3r++bbnOaU+0FR2vUS4TwIkQAKRS4AKb+ReO/Y8AgnAkphYC2sEDjfJXcZCkGPGjAlYD6JRwCfZK7BCY0KgnQznPYd9rA7nXQHO9zyPSYAESIAEoo8AXRqi75pyRCQQ8wTgOoJ4vBQSIAESIAESAAEqvPwckAAJkAAJkAAJkAAJRDUBhiWL6svLwZEACZAACZAACZAACVDh5WeABEiABEiABEiABEggqglQ4Y3qy8vBkQAJkAAJkAAJkAAJUOHlZ4AESIAESIAESIAESCCqCVDhjerLy8GRAAmQAAmQAAmQAAlQ4eVngARIgARIgARIgARIIKoJUOGN6svLwZEACZAACZAACZAACVDh5WeABEiABEiABEiABEggqglQ4Y3qy8vBkQAJkAAJkAAJkAAJUOHlZ4AESIAESIAESIAESCCqCVDhjerLy8GRAAmQAAmQAAmQAAlQ4eVngARIgARIgARIgARIIKoJ/C/nvPtCn9DOaAAAAABJRU5ErkJggg==" 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```r
```r
model <- lm(formula = explore_interactions ~ complexity * chartType * isCovidData +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)

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Analysis of Variance Table

Response: explore_interactions Df Sum Sq Mean Sq F value Pr(>F)
complexity 2 3.17 1.5830 1.1570 0.31530
chartType 1 0.05 0.0534 0.0390 0.84350
isCovidData 1 4.88 4.8777 3.5652 0.05961 .
Age 1 28.65 28.6470 20.9386 6.058e-06 ** Gender 1 4.02 4.0211 2.9391 0.08711 .
State_1 45 58.49 1.2998 0.9501 0.56721
Education 1 0.26 0.2613 0.1910 0.66227
Parents_education 1 4.21 4.2138 3.0800 0.07991 .
Language 1 0.41 0.4068 0.2973 0.58582
Ethnicity 1 8.15 8.1510 5.9577 0.01502
Income 1 0.30 0.3012 0.2202 0.63912
Religion 1 2.73 2.7283 1.9942 0.15856
trust.in.science_7 1 5.17 5.1678 3.7772 0.05254 .
need_for_cognition 1 4.03 4.0348 2.9491 0.08657 .
interpersonal.trust_1 1 5.83 5.8336 4.2638 0.03947 *
complexity:chartType 2 0.32 0.1581 0.1155 0.89092
complexity:isCovidData 2 2.50 1.2491 0.9130 0.40201
chartType:isCovidData 1 0.01 0.0086 0.0063 0.93681
complexity:chartType:isCovidData 2 2.58 1.2913 0.9439 0.38985
Residuals 476 651.24 1.3681
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

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explore_time


<!-- rnb-text-end -->


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```r
model <- lm(formula = vis.trust_6 ~ explore_time * explore_interactions * complexity * chartType * isCovidData +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)

Analysis of Variance Table

Response: vis.trust_6
                                                                    Df Sum Sq Mean Sq F value    Pr(>F)    
explore_time                                                         1   0.00   0.004  0.0035 0.9529052    
explore_interactions                                                 1   0.00   0.005  0.0039 0.9500394    
complexity                                                           2   4.51   2.254  1.8329 0.1611643    
chartType                                                            1   0.57   0.566  0.4607 0.4976676    
isCovidData                                                          1   0.26   0.257  0.2089 0.6478252    
Age                                                                  1   2.84   2.841  2.3106 0.1292104    
Gender                                                               1   3.00   2.995  2.4362 0.1192801    
State_1                                                             45  84.71   1.882  1.5310 0.0180706 *  
Education                                                            1   0.04   0.042  0.0341 0.8536374    
Parents_education                                                    1   3.19   3.192  2.5957 0.1078713    
Language                                                             1   0.13   0.126  0.1028 0.7486167    
Ethnicity                                                            1   2.48   2.478  2.0155 0.1564049    
Income                                                               1   0.11   0.109  0.0888 0.7658280    
Religion                                                             1   0.27   0.275  0.2233 0.6367864    
trust.in.science_7                                                   1  83.23  83.228 67.6893 2.185e-15 ***
need_for_cognition                                                   1   4.73   4.728  3.8449 0.0505271 .  
interpersonal.trust_1                                                1   9.66   9.655  7.8526 0.0052987 ** 
explore_time:explore_interactions                                    1   1.00   0.997  0.8110 0.3683060    
explore_time:complexity                                              2   1.13   0.567  0.4610 0.6309364    
explore_interactions:complexity                                      2   1.18   0.592  0.4812 0.6183747    
explore_time:chartType                                               1   0.06   0.058  0.0473 0.8278599    
explore_interactions:chartType                                       1   0.37   0.372  0.3022 0.5828087    
complexity:chartType                                                 2   2.05   1.027  0.8350 0.4345733    
explore_time:isCovidData                                             1   0.00   0.001  0.0006 0.9809458    
explore_interactions:isCovidData                                     1   0.15   0.153  0.1241 0.7248261    
complexity:isCovidData                                               2  17.90   8.948  7.2773 0.0007774 ***
chartType:isCovidData                                                1   0.23   0.233  0.1897 0.6633979    
explore_time:explore_interactions:complexity                         2  10.69   5.347  4.3488 0.0134827 *  
explore_time:explore_interactions:chartType                          1   7.29   7.289  5.9281 0.0152979 *  
explore_time:complexity:chartType                                    2   1.26   0.631  0.5131 0.5989682    
explore_interactions:complexity:chartType                            2   0.31   0.157  0.1277 0.8801741    
explore_time:explore_interactions:isCovidData                        1   0.78   0.781  0.6355 0.4257811    
explore_time:complexity:isCovidData                                  2   0.29   0.143  0.1166 0.8899312    
explore_interactions:complexity:isCovidData                          2   1.95   0.973  0.7915 0.4538137    
explore_time:chartType:isCovidData                                   1   0.88   0.879  0.7147 0.3983464    
explore_interactions:chartType:isCovidData                           1   2.64   2.642  2.1487 0.1434087    
complexity:chartType:isCovidData                                     2   0.52   0.260  0.2116 0.8093636    
explore_time:explore_interactions:complexity:chartType               2   1.03   0.515  0.4192 0.6578479    
explore_time:explore_interactions:complexity:isCovidData             2   0.27   0.134  0.1088 0.8969443    
explore_time:explore_interactions:chartType:isCovidData              1   1.09   1.087  0.8844 0.3475217    
explore_time:complexity:chartType:isCovidData                        2   7.63   3.815  3.1028 0.0459094 *  
explore_interactions:complexity:chartType:isCovidData                2   0.42   0.208  0.1694 0.8442673    
explore_time:explore_interactions:complexity:chartType:isCovidData   2   2.03   1.015  0.8257 0.4386077    
Residuals                                                          440 541.01   1.230                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Hypothesis: for the complex condition, we expect people who brushed more to have higher trust

```r
complexCondition <- results %>%
  filter(complexity == \complex\)

# can change the predictor to bar.vis
model<- manova(cbind(data.trust_1, 
                     data.trust_2, 
                     data.trust_3, 
                     data.trust_4, 
                     data.trust_5, 
                     data.trust_6) ~ brushed + explore_interactions + explore_time, 
               data = complexCondition)
summary.aov(model)

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Response data.trust_1 : Df Sum Sq Mean Sq F value Pr(>F) brushed 1 0.014 0.01358 0.0092 0.9236 explore_interactions 1 0.005 0.00505 0.0034 0.9534 explore_time 1 0.260 0.26008 0.1765 0.6749 Residuals 168 247.489 1.47315

Response data.trust_2 : Df Sum Sq Mean Sq F value Pr(>F) brushed 1 0.44 0.44061 0.2290 0.6329 explore_interactions 1 1.70 1.70271 0.8850 0.3482 explore_time 1 0.73 0.73208 0.3805 0.5382 Residuals 168 323.24 1.92405

Response data.trust_3 : Df Sum Sq Mean Sq F value Pr(>F) brushed 1 1.331 1.33131 0.7957 0.3737 explore_interactions 1 0.712 0.71243 0.4258 0.5149 explore_time 1 0.186 0.18588 0.1111 0.7393 Residuals 168 281.090 1.67316

Response data.trust_4 : Df Sum Sq Mean Sq F value Pr(>F) brushed 1 0.436 0.43648 0.2907 0.5905 explore_interactions 1 0.143 0.14305 0.0953 0.7580 explore_time 1 0.043 0.04309 0.0287 0.8657 Residuals 168 252.232 1.50138

Response data.trust_5 : Df Sum Sq Mean Sq F value Pr(>F) brushed 1 2.990 2.99005 1.7740 0.1847 explore_interactions 1 0.402 0.40205 0.2385 0.6259 explore_time 1 2.304 2.30354 1.3667 0.2440 Residuals 168 283.165 1.68550

Response data.trust_6 : Df Sum Sq Mean Sq F value Pr(>F) brushed 1 1.235 1.23469 0.8146 0.3680 explore_interactions 1 0.078 0.07843 0.0517 0.8203 explore_time 1 1.175 1.17467 0.7750 0.3799 Residuals 168 254.628 1.51565

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Hypothesis:
for all the conditions, we expect people who hovered more to have higher trust



<!-- rnb-text-end -->


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```r
```r
# can change the predictor to bar.vis
model<- manova(cbind(vis.trust_6, 
                     vis.trust_5, 
                     vis.trust_4, 
                     vis.trust_3, 
                     vis.trust_2, 
                     vis.trust_1) ~ brushed + explore_interactions + explore_time, 
               data = results)
summary.aov(model)

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Response vis.trust_6 : Df Sum Sq Mean Sq F value Pr(>F) brushed 1 0.22 0.22139 0.1488 0.6999 explore_interactions 1 0.02 0.02019 0.0136 0.9073 explore_time 1 0.02 0.01509 0.0101 0.9198 Residuals 540 803.63 1.48820

Response vis.trust_5 : Df Sum Sq Mean Sq F value Pr(>F) brushed 1 0.31 0.3144 0.0725 0.7878 explore_interactions 1 6.40 6.3983 1.4761 0.2249 explore_time 1 10.47 10.4729 2.4160 0.1207 Residuals 540 2340.75 4.3347

Response vis.trust_4 : Df Sum Sq Mean Sq F value Pr(>F)
brushed 1 1.91 1.9067 0.4068 0.52388
explore_interactions 1 0.67 0.6694 0.1428 0.70564
explore_time 1 13.54 13.5401 2.8887 0.08978 . Residuals 540 2531.09 4.6872
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Response vis.trust_3 : Df Sum Sq Mean Sq F value Pr(>F)
brushed 1 5.18 5.1797 3.0917 0.07926 . explore_interactions 1 1.96 1.9620 1.1711 0.27965
explore_time 1 4.17 4.1744 2.4917 0.11504
Residuals 540 904.68 1.6753
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Response vis.trust_2 : Df Sum Sq Mean Sq F value Pr(>F)
brushed 1 4.90 4.9035 3.4754 0.06283 . explore_interactions 1 0.05 0.0518 0.0367 0.84818
explore_time 1 1.50 1.5044 1.0663 0.30225
Residuals 540 761.89 1.4109
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Response vis.trust_1 : Df Sum Sq Mean Sq F value Pr(>F) brushed 1 0.21 0.20567 0.1198 0.7294 explore_interactions 1 0.08 0.08061 0.0470 0.8285 explore_time 1 0.05 0.05321 0.0310 0.8603 Residuals 540 927.10 1.71685

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# Affect on Trust {it's own section}


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```r
```r
results %>%
  gather(key = affects, value = affectRatings, affect.science_1,  affect.clarity_1, affect.aesthetic_1) %>%
  group_by(affects, chartType, isCovidData, complexity) %>%
  summarize(n = n(),
            mean = mean(affectRatings),
            se = sd(affectRatings)/sqrt(n),
            n = n()) %>%
  ggplot(aes(x = affects, y = mean, 
             ymax = mean + se, ymin = mean - se,
             colour = as.factor(isCovidData))) +
  geom_point(position = position_dodge2(width = 0.5)) +
  geom_errorbar(width = 0.5, size = 0.66, position = \dodge\) +
  # labs(title = \Trust in data\) + 
  # geom_vline(data = results %>% 
  #              group_by(complexity, isCovidData) %>%
  #              summarize(n = n(), 
  #                        vis.trust_6 = mean(vis.trust_6)), 
  #            aes(xintercept = vis.trust_6, colour = as.factor(isCovidData))) +
  facet_grid(rows = vars(chartType), cols = vars(complexity)) +
  theme_minimal() + 
  theme(panel.spacing = unit(2, \lines\))

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summarise() has grouped output by ‘affects’, ‘chartType’, ‘isCovidData’. You can override using the .groups argument.

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```r
```r
        # legend.position = \none\)
ggsave(\affectMeasures.png\, width = 17, height = 9)

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/>

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```r
```r
model <- lm(formula = affect.science_1 ~ complexity * chartType * isCovidData +
              Age + as.factor(Gender) + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)

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Analysis of Variance Table

Response: affect.science_1 Df Sum Sq Mean Sq F value Pr(>F)
complexity 2 696 348.0 1.0148 0.36326
chartType 1 492 491.9 1.4344 0.23165
isCovidData 1 362 361.5 1.0543 0.30505
Age 1 1 1.4 0.0040 0.94991
as.factor(Gender) 3 173 57.6 0.1680 0.91795
State_1 45 14488 322.0 0.9389 0.58782
Education 1 820 819.8 2.3906 0.12273
Parents_education 1 77 77.0 0.2247 0.63570
Language 1 58 58.0 0.1692 0.68101
Ethnicity 1 21 20.6 0.0600 0.80668
Income 1 351 351.4 1.0248 0.31190
Religion 1 1027 1027.0 2.9951 0.08417 .
trust.in.science_7 1 9226 9226.5 26.9067 3.17e-07 *** need_for_cognition 1 472 471.6 1.3752 0.24151
interpersonal.trust_1 1 222 221.9 0.6472 0.42150
complexity:chartType 2 61 30.6 0.0892 0.91468
complexity:isCovidData 2 591 295.7 0.8624 0.42281
chartType:isCovidData 1 589 588.8 1.7171 0.19070
complexity:chartType:isCovidData 2 585 292.3 0.8525 0.42700
Residuals 474 162538 342.9
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

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```r
```r
model <- lm(formula = affect.clarity_1 ~ complexity * chartType * isCovidData +
              Age + as.factor(Gender) + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)

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Analysis of Variance Table

Response: affect.clarity_1 Df Sum Sq Mean Sq F value Pr(>F)
complexity 2 11519 5759.6 8.5687 0.000221 chartType 1 2678 2678.4 3.9848 0.046485
isCovidData 1 1240 1240.5 1.8455 0.174956
Age 1 607 607.0 0.9030 0.342469
as.factor(Gender) 3 649 216.2 0.3217 0.809686
State_1 45 34141 758.7 1.1287 0.268328
Education 1 3168 3167.8 4.7127 0.030436 *
Parents_education 1 220 220.0 0.3274 0.567494
Language 1 97 96.8 0.1439 0.704559
Ethnicity 1 245 244.8 0.3641 0.546511
Income 1 4 4.0 0.0059 0.938582
Religion 1 137 136.9 0.2036 0.652016
trust.in.science_7 1 3107 3107.2 4.6226 0.032058 *
need_for_cognition 1 5919 5919.3 8.8063 0.003154 interpersonal.trust_1 1 87 87.1 0.1295 0.719072
complexity:chartType 2 3347 1673.4 2.4896 0.084029 .
complexity:isCovidData 2 643 321.7 0.4786 0.619950
chartType:isCovidData 1 723 722.7 1.0751 0.300320
complexity:chartType:isCovidData 2 1285 642.3 0.9556 0.385314
Residuals 474 318610 672.2
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuc3VtbWFyeShtb2RlbClcbmBgYFxuYGBgIn0= -->

```r
```r
summary(model)

<!-- rnb-source-end -->

<!-- rnb-output-begin {"data":"\nCall:\nlm(formula = affect.clarity_1 ~ complexity * chartType * isCovidData + \n    Age + as.factor(Gender) + State_1 + Education + Parents_education + \n    Language + Ethnicity + Income + Religion + trust.in.science_7 + \n    need_for_cognition + interpersonal.trust_1, data = results)\n\nResiduals:\n    Min      1Q  Median      3Q     Max \n-79.363 -10.076   6.612  16.885  49.691 \n\nCoefficients:\n                                              Estimate Std. Error t value Pr(>|t|)   \n(Intercept)                                   98.29766  159.70011   0.616  0.53851   \ncomplexitymoderate                            12.43973    5.67945   2.190  0.02899 * \ncomplexitysimple                              15.83482    5.61487   2.820  0.00500 **\nchartTypeline                                  9.68004    5.64525   1.715  0.08705 . \nisCovidData                                   -3.34656    5.99100  -0.559  0.57670   \nAge                                           -0.03664    0.08009  -0.457  0.64756   \nas.factor(Gender)2                            -0.68779    2.43226  -0.283  0.77747   \nas.factor(Gender)3                             7.53409   11.58578   0.650  0.51582   \nas.factor(Gender)5                             1.41453   26.81316   0.053  0.95795   \nState_1Arizona                                -1.41702   10.78120  -0.131  0.89549   \nState_1Arkansas                               -3.55016   14.91990  -0.238  0.81202   \nState_1California                             10.31148    7.56978   1.362  0.17379   \nState_1Colorado                                0.08705   10.56221   0.008  0.99343   \nState_1Connecticut                           -16.60690   13.70491  -1.212  0.22621   \nState_1Delaware                               -8.36370   19.89040  -0.420  0.67432   \nState_1Florida                                 5.43970    8.05673   0.675  0.49989   \nState_1Georgia                                 9.55988    8.49531   1.125  0.26103   \nState_1Hawaii                                  0.46196   16.99317   0.027  0.97832   \nState_1Illinois                               -0.57499    9.09463  -0.063  0.94962   \nState_1Indiana                                 9.08861   11.20471   0.811  0.41769   \nState_1Iowa                                   18.85560   14.90864   1.265  0.20658   \nState_1Kansas                                 -0.66151   12.11907  -0.055  0.95649   \nState_1Kentucky                               -4.08878   11.12264  -0.368  0.71333   \nState_1Louisiana                               6.06059   12.78618   0.474  0.63572   \nState_1Maine                                 -23.48927   16.69858  -1.407  0.16018   \nState_1Maryland                               11.93823    9.81675   1.216  0.22455   \nState_1Massachusetts                          -5.27871    9.84831  -0.536  0.59221   \nState_1Michigan                                5.27484    9.50903   0.555  0.57935   \nState_1Minnesota                               8.18153   13.61291   0.601  0.54812   \nState_1Mississippi                             3.60018   14.83798   0.243  0.80840   \nState_1Missouri                               -2.59586   11.16642  -0.232  0.81627   \nState_1Montana                               -34.65084   19.94973  -1.737  0.08305 . \nState_1Nebraska                                6.40950   16.73084   0.383  0.70182   \nState_1Nevada                                 27.40058   12.79224   2.142  0.03271 * \nState_1New Hampshire                         -63.23269   27.27639  -2.318  0.02086 * \nState_1New Jersey                              7.79841   11.54052   0.676  0.49953   \nState_1New York                                5.35508    8.39530   0.638  0.52387   \nState_1North Carolina                        -13.56659    9.02524  -1.503  0.13346   \nState_1Ohio                                    5.60102    9.58782   0.584  0.55938   \nState_1Oklahoma                               -2.61488   13.01374  -0.201  0.84084   \nState_1Oregon                                  9.71871   11.70982   0.830  0.40698   \nState_1Pennsylvania                           -0.84744    8.26974  -0.102  0.91842   \nState_1Rhode Island                            5.37280   16.80511   0.320  0.74933   \nState_1South Carolina                         -8.38149   13.53916  -0.619  0.53618   \nState_1South Dakota                           16.26846   27.41089   0.594  0.55313   \nState_1Tennessee                              -9.08785   10.16641  -0.894  0.37182   \nState_1Texas                                   2.72877    7.88853   0.346  0.72956   \nState_1Utah                                    7.81191   12.71358   0.614  0.53921   \nState_1Vermont                                 9.89884   19.81308   0.500  0.61758   \nState_1Virginia                               -3.13495   11.59913  -0.270  0.78707   \nState_1Washington                              0.21149    9.16761   0.023  0.98161   \nState_1West Virginia                           4.29083   16.69654   0.257  0.79730   \nState_1Wisconsin                               4.59734    9.23331   0.498  0.61878   \nState_1Wyoming                               -13.39967   27.34859  -0.490  0.62439   \nEducation                                      0.74257    0.71455   1.039  0.29924   \nParents_education                              0.62450    1.65232   0.378  0.70563   \nLanguage                                      -1.48210    4.65657  -0.318  0.75041   \nEthnicity                                      0.26015    0.65401   0.398  0.69098   \nIncome                                        -0.11960    0.25945  -0.461  0.64503   \nReligion                                       0.23786    1.23571   0.192  0.84744   \ntrust.in.science_7                             1.12682    0.61021   1.847  0.06542 . \nneed_for_cognition                             5.55210    1.94890   2.849  0.00458 **\ninterpersonal.trust_1                          0.32399    0.95307   0.340  0.73405   \ncomplexitymoderate:chartTypeline              -6.90844    8.14947  -0.848  0.39702   \ncomplexitysimple:chartTypeline               -14.02062    7.97124  -1.759  0.07924 . \ncomplexitymoderate:isCovidData                 7.87776    8.07181   0.976  0.32958   \ncomplexitysimple:isCovidData                   3.37427    8.13509   0.415  0.67849   \nchartTypeline:isCovidData                      6.66302    8.45336   0.788  0.43097   \ncomplexitymoderate:chartTypeline:isCovidData  -9.98218   11.76198  -0.849  0.39649   \ncomplexitysimple:chartTypeline:isCovidData     5.57794   11.52427   0.484  0.62860   \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\nResidual standard error: 25.93 on 474 degrees of freedom\nMultiple R-squared:  0.1797,\tAdjusted R-squared:  0.06034 \nF-statistic: 1.505 on 69 and 474 DF,  p-value: 0.008144\n"} -->

Call: lm(formula = affect.clarity_1 ~ complexity * chartType * isCovidData + Age + as.factor(Gender) + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1, data = results)

Residuals: Min 1Q Median 3Q Max -79.363 -10.076 6.612 16.885 49.691

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 98.29766 159.70011 0.616 0.53851
complexitymoderate 12.43973 5.67945 2.190 0.02899 * complexitysimple 15.83482 5.61487 2.820 0.00500 chartTypeline 9.68004 5.64525 1.715 0.08705 . isCovidData -3.34656 5.99100 -0.559 0.57670
Age -0.03664 0.08009 -0.457 0.64756
as.factor(Gender)2 -0.68779 2.43226 -0.283 0.77747
as.factor(Gender)3 7.53409 11.58578 0.650 0.51582
as.factor(Gender)5 1.41453 26.81316 0.053 0.95795
State_1Arizona -1.41702 10.78120 -0.131 0.89549
State_1Arkansas -3.55016 14.91990 -0.238 0.81202
State_1California 10.31148 7.56978 1.362 0.17379
State_1Colorado 0.08705 10.56221 0.008 0.99343
State_1Connecticut -16.60690 13.70491 -1.212 0.22621
State_1Delaware -8.36370 19.89040 -0.420 0.67432
State_1Florida 5.43970 8.05673 0.675 0.49989
State_1Georgia 9.55988 8.49531 1.125 0.26103
State_1Hawaii 0.46196 16.99317 0.027 0.97832
State_1Illinois -0.57499 9.09463 -0.063 0.94962
State_1Indiana 9.08861 11.20471 0.811 0.41769
State_1Iowa 18.85560 14.90864 1.265 0.20658
State_1Kansas -0.66151 12.11907 -0.055 0.95649
State_1Kentucky -4.08878 11.12264 -0.368 0.71333
State_1Louisiana 6.06059 12.78618 0.474 0.63572
State_1Maine -23.48927 16.69858 -1.407 0.16018
State_1Maryland 11.93823 9.81675 1.216 0.22455
State_1Massachusetts -5.27871 9.84831 -0.536 0.59221
State_1Michigan 5.27484 9.50903 0.555 0.57935
State_1Minnesota 8.18153 13.61291 0.601 0.54812
State_1Mississippi 3.60018 14.83798 0.243 0.80840
State_1Missouri -2.59586 11.16642 -0.232 0.81627
State_1Montana -34.65084 19.94973 -1.737 0.08305 . State_1Nebraska 6.40950 16.73084 0.383 0.70182
State_1Nevada 27.40058 12.79224 2.142 0.03271 * State_1New Hampshire -63.23269 27.27639 -2.318 0.02086 * State_1New Jersey 7.79841 11.54052 0.676 0.49953
State_1New York 5.35508 8.39530 0.638 0.52387
State_1North Carolina -13.56659 9.02524 -1.503 0.13346
State_1Ohio 5.60102 9.58782 0.584 0.55938
State_1Oklahoma -2.61488 13.01374 -0.201 0.84084
State_1Oregon 9.71871 11.70982 0.830 0.40698
State_1Pennsylvania -0.84744 8.26974 -0.102 0.91842
State_1Rhode Island 5.37280 16.80511 0.320 0.74933
State_1South Carolina -8.38149 13.53916 -0.619 0.53618
State_1South Dakota 16.26846 27.41089 0.594 0.55313
State_1Tennessee -9.08785 10.16641 -0.894 0.37182
State_1Texas 2.72877 7.88853 0.346 0.72956
State_1Utah 7.81191 12.71358 0.614 0.53921
State_1Vermont 9.89884 19.81308 0.500 0.61758
State_1Virginia -3.13495 11.59913 -0.270 0.78707
State_1Washington 0.21149 9.16761 0.023 0.98161
State_1West Virginia 4.29083 16.69654 0.257 0.79730
State_1Wisconsin 4.59734 9.23331 0.498 0.61878
State_1Wyoming -13.39967 27.34859 -0.490 0.62439
Education 0.74257 0.71455 1.039 0.29924
Parents_education 0.62450 1.65232 0.378 0.70563
Language -1.48210 4.65657 -0.318 0.75041
Ethnicity 0.26015 0.65401 0.398 0.69098
Income -0.11960 0.25945 -0.461 0.64503
Religion 0.23786 1.23571 0.192 0.84744
trust.in.science_7 1.12682 0.61021 1.847 0.06542 . need_for_cognition 5.55210 1.94890 2.849 0.00458 interpersonal.trust_1 0.32399 0.95307 0.340 0.73405
complexitymoderate:chartTypeline -6.90844 8.14947 -0.848 0.39702
complexitysimple:chartTypeline -14.02062 7.97124 -1.759 0.07924 . complexitymoderate:isCovidData 7.87776 8.07181 0.976 0.32958
complexitysimple:isCovidData 3.37427 8.13509 0.415 0.67849
chartTypeline:isCovidData 6.66302 8.45336 0.788 0.43097
complexitymoderate:chartTypeline:isCovidData -9.98218 11.76198 -0.849 0.39649
complexitysimple:chartTypeline:isCovidData 5.57794 11.52427 0.484 0.62860
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 25.93 on 474 degrees of freedom Multiple R-squared: 0.1797, Adjusted R-squared: 0.06034 F-statistic: 1.505 on 69 and 474 DF, p-value: 0.008144

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```r
```r
model <- lm(formula = affect.aesthetic_1 ~ complexity * chartType * isCovidData +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)

<!-- rnb-source-end -->

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Analysis of Variance Table

Response: affect.aesthetic_1 Df Sum Sq Mean Sq F value Pr(>F)
complexity 2 275 137.3 0.2691 0.764185
chartType 1 216 216.3 0.4240 0.515270
isCovidData 1 143 143.1 0.2806 0.596559
Age 1 2277 2277.2 4.4647 0.035122 * Gender 1 2234 2233.7 4.3795 0.036903 * State_1 45 18243 405.4 0.7948 0.827955
Education 1 378 377.5 0.7401 0.390050
Parents_education 1 625 625.2 1.2257 0.268801
Language 1 46 46.2 0.0906 0.763571
Ethnicity 1 556 556.5 1.0910 0.296782
Income 1 507 507.0 0.9940 0.319267
Religion 1 1 0.9 0.0017 0.966944
trust.in.science_7 1 3904 3904.4 7.6550 0.005882 ** need_for_cognition 1 2887 2886.9 5.6602 0.017748 * interpersonal.trust_1 1 2448 2448.0 4.7997 0.028949 * complexity:chartType 2 2434 1217.0 2.3861 0.093089 . complexity:isCovidData 2 557 278.7 0.5464 0.579391
chartType:isCovidData 1 1476 1476.0 2.8938 0.089576 . complexity:chartType:isCovidData 2 38 19.0 0.0372 0.963522
Residuals 476 242781 510.0
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuc3VtbWFyeShtb2RlbClcbmBgYFxuYGBgIn0= -->

```r
```r
summary(model)

<!-- rnb-source-end -->

<!-- rnb-output-begin {"data":"\nCall:\nlm(formula = affect.aesthetic_1 ~ complexity * chartType * isCovidData + \n    Age + Gender + State_1 + Education + Parents_education + \n    Language + Ethnicity + Income + Religion + trust.in.science_7 + \n    need_for_cognition + interpersonal.trust_1, data = results)\n\nResiduals:\n    Min      1Q  Median      3Q     Max \n-69.275 -12.197   1.691  15.044  45.767 \n\nCoefficients:\n                                               Estimate Std. Error t value Pr(>|t|)   \n(Intercept)                                  -343.18480  138.53967  -2.477  0.01359 * \ncomplexitymoderate                             -4.64381    4.94707  -0.939  0.34836   \ncomplexitysimple                               -5.65000    4.89075  -1.155  0.24857   \nchartTypeline                                  -7.34731    4.91736  -1.494  0.13580   \nisCovidData                                    -4.91980    5.21785  -0.943  0.34622   \nAge                                             0.19631    0.06950   2.825  0.00493 **\nGender                                         -4.46005    1.94088  -2.298  0.02200 * \nState_1Arizona                                 -7.70625    9.38801  -0.821  0.41214   \nState_1Arkansas                                 9.54163   12.99591   0.734  0.46319   \nState_1California                              -0.84290    6.58797  -0.128  0.89825   \nState_1Colorado                                -0.12965    9.15381  -0.014  0.98871   \nState_1Connecticut                            -13.60102   11.93321  -1.140  0.25496   \nState_1Delaware                               -13.80856   17.32074  -0.797  0.42572   \nState_1Florida                                 -6.44689    7.01727  -0.919  0.35871   \nState_1Georgia                                 -5.49618    7.39898  -0.743  0.45795   \nState_1Hawaii                                  10.20125   14.79687   0.689  0.49090   \nState_1Illinois                                -0.31430    7.86413  -0.040  0.96814   \nState_1Indiana                                  3.55061    9.75604   0.364  0.71606   \nState_1Iowa                                    -2.52722   12.98262  -0.195  0.84574   \nState_1Kansas                                   9.07849   10.55249   0.860  0.39005   \nState_1Kentucky                                -2.45508    9.68638  -0.253  0.80002   \nState_1Louisiana                                2.66259   11.13651   0.239  0.81114   \nState_1Maine                                    7.72587   14.53748   0.531  0.59536   \nState_1Maryland                                 3.20737    8.55124   0.375  0.70777   \nState_1Massachusetts                            7.96405    8.57676   0.929  0.35359   \nState_1Michigan                                -6.74239    8.28184  -0.814  0.41598   \nState_1Minnesota                               -4.01388   11.85757  -0.339  0.73513   \nState_1Mississippi                             -5.48221   12.91630  -0.424  0.67144   \nState_1Missouri                                -4.36157    9.64818  -0.452  0.65143   \nState_1Montana                                 10.34406   17.37623   0.595  0.55193   \nState_1Nebraska                               -11.82145   14.57115  -0.811  0.41760   \nState_1Nevada                                  -8.20670   11.14301  -0.736  0.46180   \nState_1New Hampshire                           24.29003   23.74061   1.023  0.30676   \nState_1New Jersey                              -4.48929   10.04522  -0.447  0.65514   \nState_1New York                                -6.69492    7.31042  -0.916  0.36023   \nState_1North Carolina                          -8.40750    7.85876  -1.070  0.28524   \nState_1Ohio                                    -8.07746    8.35032  -0.967  0.33387   \nState_1Oklahoma                                 6.69427   11.23780   0.596  0.55166   \nState_1Oregon                                   1.44363   10.19863   0.142  0.88749   \nState_1Pennsylvania                             2.54905    7.19235   0.354  0.72319   \nState_1Rhode Island                           -24.84510   14.63861  -1.697  0.09031 . \nState_1South Carolina                          -7.96101   11.79358  -0.675  0.49998   \nState_1South Dakota                           -39.20021   23.87625  -1.642  0.10129   \nState_1Tennessee                               -7.41244    8.85314  -0.837  0.40286   \nState_1Texas                                   -2.16939    6.86979  -0.316  0.75230   \nState_1Utah                                    -9.21788   11.07341  -0.832  0.40558   \nState_1Vermont                                -16.60592   17.25466  -0.962  0.33634   \nState_1Virginia                               -14.01299   10.10358  -1.387  0.16611   \nState_1Washington                              -6.87525    7.98370  -0.861  0.38958   \nState_1West Virginia                            9.45129   14.54213   0.650  0.51605   \nState_1Wisconsin                               -3.53710    8.04219  -0.440  0.66027   \nState_1Wyoming                                  5.25649   23.81010   0.221  0.82537   \nEducation                                      -0.09478    0.62139  -0.153  0.87883   \nParents_education                              -1.74172    1.43913  -1.210  0.22678   \nLanguage                                        2.02793    4.05567   0.500  0.61729   \nEthnicity                                       0.30928    0.56782   0.545  0.58623   \nIncome                                          0.15415    0.22469   0.686  0.49302   \nReligion                                       -0.47930    1.07540  -0.446  0.65602   \ntrust.in.science_7                              1.01709    0.53127   1.914  0.05616 . \nneed_for_cognition                              3.19323    1.68624   1.894  0.05887 . \ninterpersonal.trust_1                           1.83396    0.82762   2.216  0.02717 * \ncomplexitymoderate:chartTypeline                9.65778    7.09203   1.362  0.17391   \ncomplexitysimple:chartTypeline                  6.11217    6.94146   0.881  0.37902   \ncomplexitymoderate:isCovidData                 -1.82717    7.01866  -0.260  0.79472   \ncomplexitysimple:isCovidData                    4.32833    7.07960   0.611  0.54124   \nchartTypeline:isCovidData                       6.95523    7.36274   0.945  0.34532   \ncomplexitymoderate:chartTypeline:isCovidData    1.49074   10.21876   0.146  0.88408   \ncomplexitysimple:chartTypeline:isCovidData     -1.19256   10.03678  -0.119  0.90547   \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\nResidual standard error: 22.58 on 476 degrees of freedom\nMultiple R-squared:  0.1392,\tAdjusted R-squared:  0.01799 \nF-statistic: 1.148 on 67 and 476 DF,  p-value: 0.2094\n"} -->

Call: lm(formula = affect.aesthetic_1 ~ complexity * chartType * isCovidData + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1, data = results)

Residuals: Min 1Q Median 3Q Max -69.275 -12.197 1.691 15.044 45.767

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) -343.18480 138.53967 -2.477 0.01359 * complexitymoderate -4.64381 4.94707 -0.939 0.34836
complexitysimple -5.65000 4.89075 -1.155 0.24857
chartTypeline -7.34731 4.91736 -1.494 0.13580
isCovidData -4.91980 5.21785 -0.943 0.34622
Age 0.19631 0.06950 2.825 0.00493 ** Gender -4.46005 1.94088 -2.298 0.02200 * State_1Arizona -7.70625 9.38801 -0.821 0.41214
State_1Arkansas 9.54163 12.99591 0.734 0.46319
State_1California -0.84290 6.58797 -0.128 0.89825
State_1Colorado -0.12965 9.15381 -0.014 0.98871
State_1Connecticut -13.60102 11.93321 -1.140 0.25496
State_1Delaware -13.80856 17.32074 -0.797 0.42572
State_1Florida -6.44689 7.01727 -0.919 0.35871
State_1Georgia -5.49618 7.39898 -0.743 0.45795
State_1Hawaii 10.20125 14.79687 0.689 0.49090
State_1Illinois -0.31430 7.86413 -0.040 0.96814
State_1Indiana 3.55061 9.75604 0.364 0.71606
State_1Iowa -2.52722 12.98262 -0.195 0.84574
State_1Kansas 9.07849 10.55249 0.860 0.39005
State_1Kentucky -2.45508 9.68638 -0.253 0.80002
State_1Louisiana 2.66259 11.13651 0.239 0.81114
State_1Maine 7.72587 14.53748 0.531 0.59536
State_1Maryland 3.20737 8.55124 0.375 0.70777
State_1Massachusetts 7.96405 8.57676 0.929 0.35359
State_1Michigan -6.74239 8.28184 -0.814 0.41598
State_1Minnesota -4.01388 11.85757 -0.339 0.73513
State_1Mississippi -5.48221 12.91630 -0.424 0.67144
State_1Missouri -4.36157 9.64818 -0.452 0.65143
State_1Montana 10.34406 17.37623 0.595 0.55193
State_1Nebraska -11.82145 14.57115 -0.811 0.41760
State_1Nevada -8.20670 11.14301 -0.736 0.46180
State_1New Hampshire 24.29003 23.74061 1.023 0.30676
State_1New Jersey -4.48929 10.04522 -0.447 0.65514
State_1New York -6.69492 7.31042 -0.916 0.36023
State_1North Carolina -8.40750 7.85876 -1.070 0.28524
State_1Ohio -8.07746 8.35032 -0.967 0.33387
State_1Oklahoma 6.69427 11.23780 0.596 0.55166
State_1Oregon 1.44363 10.19863 0.142 0.88749
State_1Pennsylvania 2.54905 7.19235 0.354 0.72319
State_1Rhode Island -24.84510 14.63861 -1.697 0.09031 . State_1South Carolina -7.96101 11.79358 -0.675 0.49998
State_1South Dakota -39.20021 23.87625 -1.642 0.10129
State_1Tennessee -7.41244 8.85314 -0.837 0.40286
State_1Texas -2.16939 6.86979 -0.316 0.75230
State_1Utah -9.21788 11.07341 -0.832 0.40558
State_1Vermont -16.60592 17.25466 -0.962 0.33634
State_1Virginia -14.01299 10.10358 -1.387 0.16611
State_1Washington -6.87525 7.98370 -0.861 0.38958
State_1West Virginia 9.45129 14.54213 0.650 0.51605
State_1Wisconsin -3.53710 8.04219 -0.440 0.66027
State_1Wyoming 5.25649 23.81010 0.221 0.82537
Education -0.09478 0.62139 -0.153 0.87883
Parents_education -1.74172 1.43913 -1.210 0.22678
Language 2.02793 4.05567 0.500 0.61729
Ethnicity 0.30928 0.56782 0.545 0.58623
Income 0.15415 0.22469 0.686 0.49302
Religion -0.47930 1.07540 -0.446 0.65602
trust.in.science_7 1.01709 0.53127 1.914 0.05616 . need_for_cognition 3.19323 1.68624 1.894 0.05887 . interpersonal.trust_1 1.83396 0.82762 2.216 0.02717 * complexitymoderate:chartTypeline 9.65778 7.09203 1.362 0.17391
complexitysimple:chartTypeline 6.11217 6.94146 0.881 0.37902
complexitymoderate:isCovidData -1.82717 7.01866 -0.260 0.79472
complexitysimple:isCovidData 4.32833 7.07960 0.611 0.54124
chartTypeline:isCovidData 6.95523 7.36274 0.945 0.34532
complexitymoderate:chartTypeline:isCovidData 1.49074 10.21876 0.146 0.88408
complexitysimple:chartTypeline:isCovidData -1.19256 10.03678 -0.119 0.90547
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 22.58 on 476 degrees of freedom Multiple R-squared: 0.1392, Adjusted R-squared: 0.01799 F-statistic: 1.148 on 67 and 476 DF, p-value: 0.2094

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Trust in Vis



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<!-- rnb-source-begin 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 -->

```r
model <- lm(formula = vis.trust_4 ~  vis.trust_1 + vis.trust_2 + vis.trust_3 + vis.trust_6 +
              data.trust_1 + data.trust_2 + data.trust_3 + data.trust_4 + data.trust_5 + data.trust_6 + 
              affect.science_1 + affect.clarity_1 + affect.aesthetic_1 + 
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
summary(model)

Call:
lm(formula = vis.trust_4 ~ vis.trust_1 + vis.trust_2 + vis.trust_3 + 
    vis.trust_6 + data.trust_1 + data.trust_2 + data.trust_3 + 
    data.trust_4 + data.trust_5 + data.trust_6 + affect.science_1 + 
    affect.clarity_1 + affect.aesthetic_1 + Age + Gender + State_1 + 
    Education + Parents_education + Language + Ethnicity + Income + 
    Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1, 
    data = results)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1192 -1.5458 -0.1231  1.6803  5.0100 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)   
(Intercept)            2.7360875 13.2493055   0.207  0.83648   
vis.trust_1            0.1774823  0.0901378   1.969  0.04953 * 
vis.trust_2           -0.1436682  0.1000904  -1.435  0.15184   
vis.trust_3            0.2052010  0.0882952   2.324  0.02055 * 
vis.trust_6            0.0106596  0.1256406   0.085  0.93242   
data.trust_1          -0.0754750  0.1147079  -0.658  0.51087   
data.trust_2           0.0728709  0.0754791   0.965  0.33481   
data.trust_3          -0.0733742  0.0992398  -0.739  0.46005   
data.trust_4          -0.1609609  0.1076849  -1.495  0.13565   
data.trust_5           0.1796598  0.0942146   1.907  0.05714 . 
data.trust_6           0.1202116  0.1097296   1.096  0.27384   
affect.science_1       0.0041559  0.0057901   0.718  0.47326   
affect.clarity_1       0.0119407  0.0044146   2.705  0.00708 **
affect.aesthetic_1    -0.0091538  0.0047816  -1.914  0.05617 . 
Age                   -0.0007747  0.0066660  -0.116  0.90753   
Gender                -0.0334694  0.1821503  -0.184  0.85429   
State_1Arizona         0.5781813  0.8818171   0.656  0.51235   
State_1Arkansas        0.1654851  1.2153042   0.136  0.89175   
State_1California      0.6710779  0.6186370   1.085  0.27858   
State_1Colorado        1.6087993  0.8545289   1.883  0.06036 . 
State_1Connecticut     0.6014914  1.1348667   0.530  0.59635   
State_1Delaware        0.4127931  1.6367854   0.252  0.80100   
State_1Florida        -0.0645839  0.6598028  -0.098  0.92207   
State_1Georgia         0.6357072  0.6973462   0.912  0.36244   
State_1Hawaii          1.5158014  1.4292139   1.061  0.28942   
State_1Illinois        0.4114517  0.7412135   0.555  0.57908   
State_1Indiana         0.9054184  0.9319751   0.972  0.33179   
State_1Iowa            0.5895093  1.2258566   0.481  0.63081   
State_1Kansas         -0.1169873  0.9820572  -0.119  0.90523   
State_1Kentucky        0.8533902  0.9159495   0.932  0.35197   
State_1Louisiana       1.4766920  1.0508013   1.405  0.16059   
State_1Maine           2.0675311  1.3639204   1.516  0.13022   
State_1Maryland        0.7295911  0.8012389   0.911  0.36298   
State_1Massachusetts   0.9138758  0.8065999   1.133  0.25779   
State_1Michigan       -0.4089579  0.7777781  -0.526  0.59927   
State_1Minnesota       1.4713793  1.1150752   1.320  0.18763   
State_1Mississippi     1.1850918  1.2121032   0.978  0.32871   
State_1Missouri        0.3464118  0.9117750   0.380  0.70417   
State_1Montana         0.9619297  1.6434887   0.585  0.55863   
State_1Nebraska       -0.4262021  1.3656965  -0.312  0.75512   
State_1Nevada         -0.8104383  1.0489877  -0.773  0.44015   
State_1New Hampshire   4.4947996  2.3505690   1.912  0.05645 . 
State_1New Jersey      1.6922151  0.9487942   1.784  0.07514 . 
State_1New York        0.2338256  0.6883296   0.340  0.73423   
State_1North Carolina  0.5217654  0.7354990   0.709  0.47842   
State_1Ohio            1.3984928  0.7872410   1.776  0.07630 . 
State_1Oklahoma        0.9939442  1.0539795   0.943  0.34614   
State_1Oregon          0.7134429  0.9477105   0.753  0.45194   
State_1Pennsylvania    0.3339884  0.6766332   0.494  0.62182   
State_1Rhode Island    1.5984759  1.3812082   1.157  0.24773   
State_1South Carolina  1.1428656  1.1237264   1.017  0.30966   
State_1South Dakota   -2.8785038  2.3336971  -1.233  0.21802   
State_1Tennessee       0.5359354  0.8423040   0.636  0.52491   
State_1Texas           0.7332301  0.6469186   1.133  0.25761   
State_1Utah            0.9671576  1.0450723   0.925  0.35521   
State_1Vermont         1.1176656  1.6393914   0.682  0.49573   
State_1Virginia        0.9833051  0.9538116   1.031  0.30310   
State_1Washington      1.2430463  0.7520239   1.653  0.09901 . 
State_1West Virginia   0.1485907  1.3602187   0.109  0.91306   
State_1Wisconsin       1.0229755  0.7534204   1.358  0.17518   
State_1Wyoming        -0.0535624  2.2228018  -0.024  0.98079   
Education             -0.0571436  0.0584352  -0.978  0.32862   
Parents_education     -0.1686324  0.1341910  -1.257  0.20950   
Language               0.4228430  0.3777062   1.120  0.26349   
Ethnicity              0.0703587  0.0534961   1.315  0.18907   
Income                -0.0285771  0.0212530  -1.345  0.17939   
Religion              -0.1665831  0.1003878  -1.659  0.09770 . 
trust.in.science_7     0.0683751  0.0532900   1.283  0.20009   
need_for_cognition     0.1014821  0.1633302   0.621  0.53468   
interpersonal.trust_1 -0.0386799  0.0789117  -0.490  0.62424   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.124 on 474 degrees of freedom
Multiple R-squared:  0.1604,    Adjusted R-squared:  0.03821 
F-statistic: 1.313 on 69 and 474 DF,  p-value: 0.05623

emmeans(aov(vis.trust_6 ~ affect.science_1 * affect.clarity_1 , data = results) , ~ affect.clarity_1)

NOTE: Results may be misleading due to involvement in interactions

 affect.clarity_1 emmean     SE  df lower.CL upper.CL
             75.6    4.5 0.0497 540      4.4      4.6

Confidence level used: 0.95 

Trust in Data

```r
model <- lm(formula = data.trust_6 ~ affect.science_1 * affect.clarity_1 * affect.aesthetic_1 +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)

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Analysis of Variance Table

Response: data.trust_6 Df Sum Sq Mean Sq F value Pr(>F)
affect.science_1 1 89.71 89.711 67.9000 1.652e-15 affect.clarity_1 1 17.73 17.733 13.4220 0.0002764 affect.aesthetic_1 1 9.82 9.822 7.4341 0.0066341 ** Age 1 0.47 0.470 0.3557 0.5511621
Gender 1 0.03 0.034 0.0255 0.8732163
State_1 45 99.16 2.203 1.6678 0.0054312 ** Education 1 0.02 0.020 0.0151 0.9024074
Parents_education 1 4.54 4.538 3.4350 0.0644403 .
Language 1 0.08 0.076 0.0575 0.8105316
Ethnicity 1 0.00 0.000 0.0000 0.9964490
Income 1 2.69 2.691 2.0370 0.1541610
Religion 1 0.81 0.811 0.6137 0.4338006
trust.in.science_7 1 27.74 27.744 20.9991 5.866e-06 * need_for_cognition 1 2.71 2.715 2.0548 0.1523801
interpersonal.trust_1 1 11.00 11.003 8.3280 0.0040796 affect.science_1:affect.clarity_1 1 0.52 0.520 0.3936 0.5307322
affect.science_1:affect.aesthetic_1 1 3.22 3.218 2.4356 0.1192669
affect.clarity_1:affect.aesthetic_1 1 0.34 0.341 0.2578 0.6118972
affect.science_1:affect.clarity_1:affect.aesthetic_1 1 0.70 0.696 0.5271 0.4681710
Residuals 480 634.18 1.321
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

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```r
```r
emmeans(aov(data.trust_6 ~ affect.science_1 * affect.clarity_1 , data = results) , ~ affect.clarity_1)

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NOTE: Results may be misleading due to involvement in interactions affect.clarity_1 emmean SE df lower.CL upper.CL 75.6 4.41 0.054 540 4.31 4.52

Confidence level used: 0.95

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# Factor Analysis 



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```r
```r
factorAnalysis <- results %>%
  select(data.trust_1, data.trust_2, data.trust_3, data.trust_4, data.trust_5, data.trust_6,
         vis.trust_1, vis.trust_2, vis.trust_3, vis.trust_4, vis.trust_5, vis.trust_6,
         trust.in.science_1, trust.in.science_2, trust.in.science_3, trust.in.science_4, trust.in.science_5,
         trust.in.science_6, trust.in.science_7, trust.in.science_8, 
         cognition_1, cognition_2, cognition_3, cognition_4, cognition_5, cognition_6,
         # brushed, explore_interactions, # explore_time, 
         interpersonal.trust_1,
         vlat_simple, vlat_moderate, vlat_complex,
         # initial impression
         affect.science_1, affect.clarity_1, affect.aesthetic_1)

nfactors(factorAnalysis)

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Number of factors Call: vss(x = x, n = n, rotate = rotate, diagonal = diagonal, fm = fm, n.obs = n.obs, plot = FALSE, title = title, use = use, cor = cor) VSS complexity 1 achieves a maximimum of 0.7 with 3 factors VSS complexity 2 achieves a maximimum of 0.78 with 4 factors The Velicer MAP achieves a minimum of 0.01 with 3 factors Empirical BIC achieves a minimum of -1558.7 with 7 factors Sample Size adjusted BIC achieves a minimum of -409.76 with 8 factors

Statistics by number of factors

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" 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<!-- rnb-text-begin -->


Factor 4 seem to have minimum compelxity, BIC is pretty low, and big jump for root mean
5 seems meh becuase of the big jump from 4-5 on complexity. 


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```r
```r
f7 <- fa(factorAnalysis, 7)
pdf(file = \f7.pdf\,   # The directory you want to save the file in
    width = 15, # The width of the plot in inches
    height = 23) # The height of the plot in inches
fa.diagram(f7)
dev.off()

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null device 1

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```r
```r
# based on the factor analysis, it looks like not all the vis Qs go together and not all the data Qs go together. 

```